Optimized Predictive Models in Health Care Using Machine Learning E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: * provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; * explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; * gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; * emphasizes validating and evaluating predictive models; * provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; * discusses the challenges and limitations of predictive modeling in healthcare; * highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Impact of Technology on Daily Food Habits and Their Effects on Health
1.1 Introduction
1.2 Technologies, Foodies, and Consciousness
1.3 Government Programs to Encourage Healthy Choices
1.4 Technology’s Impact on Our Food Consumption
1.5 Customized Food is the Future of Food
1.6 Impact of Food Technology and Innovation on Nutrition and Health
1.7 Top Prominent and Emerging Food Technology Trends
1.8 Discussion
1.9 Conclusions
References
2 Issues in Healthcare and the Role of Machine Learning in Healthcare
2.1 Introduction
2.2 Issues in Healthcare
2.3 Factors Affecting the Health
2.4 Machine Learning in Healthcare
2.5 Conclusion
References
3 Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks
3.1 Introduction
3.2 Literature Survey
3.3 Proposed Methodology
3.4 Result and Discussion
3.5 Conclusion and Future Scope
References
4 Analysis of Smart Technologies in Healthcare
4.1 Introduction
4.2 Emerging Technologies in Healthcare
4.3 Literature Review
4.4 Risks and Challenges
4.5 Conclusion
References
5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease
5.1 Introduction
5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles
5.3 Experimental Work and Results
5.4 Conclusion
References
6 Feature Selection for Breast Cancer Detection
6.1 Introduction
6.2 Literature Review
6.3 Design and Implementation
6.4 Conclusion
References
7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Results and Discussions
7.5 Conclusion
References
8 A Robust Machine Learning Model for Breast Cancer Prediction
8.1 Introduction
8.2 Literature Review
8.3 Proposed Mythology
8.4 Result and Discussion
8.5 Concluding Remarks and Future Scope
References
9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks
9.1 Introduction
9.2 Literature Work
9.3 Proposed Section
9.4 Result Analysis
9.5 Conclusion and Future Scope
References
10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms
10.1 Introduction
10.2 Related Works
10.3 Proposed Methodology
10.4 Result and Discussions
10.5 Conclusion
References
11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning
11.1 Introduction
11.2 Literature Review
11.3 Proposed Methodology
11.4 Results and Discussion
11.5 Concluding Remarks and Future Scope
References
12 Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare
12.1 Introduction
12.2 Literature Survey
12.3 Proposed System
12.4 Results and Discussion
12.5 Conclusion and Future Scope
References
13 NLP-Based Speech Analysis Using K-Neighbor Classifier
13.1 Introduction
13.2 Supervised Machine Learning for NLP and Text Analytics
13.3 Unsupervised Machine Learning for NLP and Text Analytics
13.4 Experiments and Results
13.5 Conclusion
References
14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction
14.1 Introduction
14.2 Literature Review
14.3 Materials and Methods
14.4 Result Analysis
14.5 Conclusion
References
15 Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges
15.1 Introduction
15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare
15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics
15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases
15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems
15.6 Conclusion
References
16 Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue
16.1 Introduction
16.2 Proposed Framework “Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)”
16.3 Potential Impact
16.4 Discussion and Limitations
16.5 Future Work
16.6 Conclusion
References
17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering
17.1 Introduction
17.2 Literature Review
17.3 Proposed Methodology
17.4 Implications
17.5 Conclusion
17.6 Limitations and Scope of Future Work
References
18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer
18.1 Introduction
18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer
18.3 Experimental Work and Comparison Analysis
18.4 Conclusion
References
19 Analysis of Business Intelligence in Healthcare Using Machine Learning
19.1 Introduction
19.2 Data Gathering
19.3 Literature Review
19.4 Research Methodology
19.5 Implementation
19.6 Eligibility Criteria
19.7 Results
19.8 Conclusion and Future Scope
References
20 StressDetect: ML for Mental Stress Prediction
20.1 Introduction
20.2 Related Work
20.3 Materials and Methods
20.4 Results
20.5 Discussion & Conclusions
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Cyber attack on various levels.
Chapter 3
Table 3.1 Comparison of literature survey on the proposed method.
Table 3.2 Accuracy of different classification techniques and proposed techniq...
Chapter 7
Table 7.1 Performance with actual features.
Table 7.2 Performance with prominent features.
Chapter 8
Table 8.1 Study of existing methodologies.
Chapter 9
Table 9.1 Observations for trained model for 3 epoch.
Table 9.2 Observations for trained model for 20 epoch.
Table 9.3 Accuracy after hyper parameter tunning.
Chapter 10
Table 10.1 A review of the literature of the previous research paper based on ...
Table 10.2 Summary stats of a variable dataset.
Table 10.3 Consistent dataset summary statistics.
Chapter 11
Table 11.1 Review of different methods currently in use.
Table 11.2 Ranking of important features using correlation analysis.
Table 11.3 Ranking of important features using principal component analysis.
Table 11.4 Ranking of important features using ReliefF attribute evaluator.
Table 11.5 Correlation matrix of elements in principal component analysis.
Chapter 12
Table 12.1 Comparison of state-of-art methods.
Chapter 13
Table 13.1 Important features with values.
Chapter 14
Table 14.1 Evaluation of logistic regression.
Table 14.2 Evaluation of Naive Bayes.
Table 14.3 Evaluation of random forest classifier.
Table 14.4 Accuracy analysis for LR, NB, RF.
Table 14.5 Result of ensembling.
Chapter 15
Table 15.1 Summary of the ML-based modelling for detecting the breast cancer d...
Table 15.2 Taxonomy of the ML-based modelling for detecting health issues/dise...
Chapter 16
Table 16.1 List of existing models for fatigue detection using AI and their re...
Table 16.2 Epochs and layers.
Table 16.3 Result analysis of existing models.
Table 16.4 Result Analysis of existing vs. proposed models.
Chapter 17
Table 17.1 Algorithm types.
Table 17.2 Terms and definitions.
Chapter 18
Table 18.1 Comparison of prediction accuracy.
Chapter 19
Table 19.1 Literature review table.
Table 19.2 Inclusion/exclusion criteria table.
Chapter 20
Table 20.1 Materials and methods.
Table 20.2 Effectiveness of various machine learning models in forecasting str...
List of Illustrations
Chapter 1
Figure 1.1 Role of food habits on our mental health.
Figure 1.2 Macronutrients and micronutrients.
Figure 1.3 Technological innovations in food sector.
Figure 1.4 Food on social media.
Figure 1.5 Impact of technology on our food consumption.
Figure 1.6 Food customization.
Figure 1.7 Impact of food technology and innovation on nutrition and health.
Figure 1.8 Different types of gluten-free grains.
Figure 1.9 Alternative protein market map.
Figure 1.10 Plant-based meat and protein substitutes.
Figure 1.11 Market size of functional food ingredients.
Figure 1.12 Personalized food as per individuals.
Figure 1.13 Digital food management.
Figure 1.14 E-commerce platforms
.
Figure 1.15 3D food printing.
Figure 1.16 Robotics in food industry.
Figure 1.17 Food transparency with clean food labels.
Chapter 2
Figure 2.1 Issues in healthcare.
Figure 2.2 Privacy issues in healthcare.
Chapter 3
Figure 3.1 Workflow of proposed work.
Figure 3.2 Feature extraction and classification in CNN.
Figure 3.3 Architectural diagram of proposed CNN.
Figure 3.4 An overview of CNN architecture.
Figure 3.5 Validation accuracy of proposed work on 40 epoch size.
Figure 3.6 Results of validation accuracy and validation loss on 40 epoch size...
Figure 3.7 Results of proposed work on 40 epoch size.
Figure 3.8 Validation accuracy of proposed work on 60 epoch size.
Figure 3.9 Results of proposed work on 40 epoch size.
Figure 3.10 Results of validation accuracy and validation loss on 60 epoch siz...
Figure 3.11 Wide comparison of accuracy of different classification techniques...
Chapter 5
Figure 5.1 Architecture of proposed approach for lung cancer disease diagnosis...
Figure 5.2 Model for MLMR preprocessing.
Figure 5.3 Diagnosing accuracy with 10000 data.
Chapter 6
Figure 6.1 Correlation graph.
Figure 6.2 Feature minimization.
Figure 6.3 Technique used for specific dataset features concerning the accurac...
Figure 6.4 Test results.
Figure 6.5 Accuracy of the feature selection methods employed.
Figure 6.6 Feature importance concerning the final result of our classificatio...
Figure 6.7 Comparative analysis of all techniques.
Chapter 7
Figure 7.1 Research methodology.
Figure 7.2 Accuracy analysis comparison between actual and prominent feature....
Figure 7.3 Accuracy comparison of prominent features.
Figure 7.4 Comparison of error between prominent features.
Figure 7.5 Comparison of TP Rate between prominent features.
Figure 7.6 Comparison of FP Rate between prominent features.
Figure 7.7 Confusion matrix of predicted and actual class.
Chapter 8
Figure 8.1 Proposed mythology.
Figure 8.2 Traditional machine learning model.
Figure 8.3 Robust machine learning model.
Figure 8.4 Confusion matrix.
Figure 8.5 Accuracy comparison of proposed work.
Figure 8.6 Error comparison of proposed work.
Figure 8.7 FP rate comparison of proposed work.
Figure 8.8 TP rate comparison of proposed work.
Figure 8.9 F measure comparison of proposed work.
Chapter 9
Figure 9.1 Overall flow of proposed work.
Figure 9.2 Learning rate of ResNet50.
Figure 9.3 Accuracy after training model on 3 epoch.
Figure 9.4 Accuracy after training model on 3 epoch.
Figure 9.5 Confusion matrix on proposed work.
Figure 9.6 Accuracy after hyper parameter tunning.
Chapter 10
Figure 10.1 3D representation of dataset’s parameters.
Figure 10.2 Accuracy of various classifiers between inconsistent and consisten...
Figure 10.3 CC of various classifiers between inconsistent and consistent data...
Figure 10.4 Mean absolute error (MAE) of classifiers between inconsistent and ...
Chapter 11
Figure 11.1 Illustrates the proposed workflow, which shows a preprocessing pha...
Figure 11.2 Ranking of important features using correlation analysis.
Figure 11.3 Ranking of important features using principal component analysis....
Figure 11.4 Ranking of important features using ReliefF attribute evaluator.
Chapter 12
Figure 12.1 Architecture of proposed system.
Figure 12.2 (a) Main frame of the application. (b) Uploading dataset.
Figure 12.3 Calculating no. of parameters.
Figure 12.4 Generating model.
Figure 12.5 KNN algorithm implementation.
Figure 12.6 Implementation of random forest algorithm.
Figure 12.7 Implementation of multilayer perception algorithm.
Figure 12.8 Comparison of three algorithms.
Figure 12.9 Authenticating user.
Figure 12.10 Predicting user data in the application.
Chapter 13
Figure 13.1 Graphical view with their class level.
Figure 13.2 Class color view with coordinates.
Figure 13.3 Class view with plot matrix.
Figure 13.4 Graphical view with their instances.
Figure 13.5 Standardized parameter result.
Figure 13.6 Classifier comparative results.
Chapter 14
Figure 14.1 Proposed model.
Figure 14.2 Correlation of dataset attributes.
Figure 14.3 Accuracy graph for LR, NB, RF.
Chapter 15
Figure 15.1 Classification of A.L., ML, and DL.
Figure 15.2 Major applications of A.I., ML, and DL in healthcare.
Chapter 16
Figure 16.1 System design of CIFDPF.
Figure 16.2 Overall CIFDPF architecture.
Figure 16.3 Architecture of CNN.
Figure 16.4 RNN with Xt as input and Yt as output at timestep t.
Figure 16.5 Flowchart of CIFDPF model for fatigue detection activation functio...
Figure 16.6 Accuracy chart.
Figure 16.7 Accuracy chart with samples.
Figure 16.8 Confusion matrix.
Chapter 18
Figure 18.1 Architecture of proposed TP-LSTM technique.
Figure 18.2 Deep artificial feed-forward neural network model.
Figure 18.3 Construction of the LSTM model.
Chapter 19
Figure 19.1 Business intelligence architecture [5].
Figure 19.2 ETL framework [4].
Figure 19.3 Steps involved in research methodology.
Figure 19.4 Snippet of data sample.
Figure 19.5 Snippet1 of the data model.
Figure 19.6 Graph of proposed work.
Chapter 20
Figure 20.1 A general framework for detecting stress using machine learning.
Figure 20.2 Assessment of various machine learning models in forecasting stres...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Optimized Predictive Models in Healthcare Using Machine Learning
Edited by
Sandeep Kumar
CSE Department, Koneru Lakshmaiah Education Vaddeswaram, Andhra Pradesh, India
Anuj Sharma
Maharshi Dayanand University, Rohtak, India
Navneet Kaur
Chandigarh University, Gharuan, Mohali, India
Lokesh Pawar
Chandigarh University, Gharuan, Mohali, India
and
Rohit Bajaj
Chandigarh University, Gharuan, Mohali, India
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-17462-1
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
This book provides more relevant information on optimized predictive models in healthcare using machine learning. As a resource for students, academics, and researchers from the industry who wish to know more about real-time applications, it focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of optimized predictive model design, evaluation, and user diversity. Going beyond descriptions of rehabilitation methods for specific processes, it explains the underlying causes of the social and organizational problems. This book describes new algorithms for modeling that are now accessible to scientists of all varieties. The healthcare industry faces an unprecedented challenge to provide efficient and cost-effective care while maintaining high patient satisfaction. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers to make informed decisions, resulting in better patient outcomes and reduced costs. This book offers a comprehensive guide to developing and implementing optimized predictive models in healthcare and is intended for healthcare professionals, data scientists, and researchers interested in using predictive modeling to improve patient care and outcomes.
One of the critical features of this book is its practical approach to developing predictive models in healthcare. The authors have provided detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data. The book also discusses feature selection and engineering, explaining how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models. In addition, the book gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application.
Another essential feature of this book is its emphasis on validating and evaluating predictive models. It explains the importance of validating predictive models to ensure their accuracy and reliability and describes how to evaluate the performance of predictive models using a range of metrics. By providing a comprehensive overview of validation and evaluation techniques, readers can develop predictive models that are both accurate and reliable. The book also includes a chapter on applications of predictive modeling in healthcare, which offers real-world examples of how predictive models can improve patient outcomes. Other topics discussed include various other applications, including disease diagnosis, drug development, and patient monitoring. By highlighting these success stories, the book demonstrates the potential impact of predictive modeling on healthcare.
Another key feature of this book is its discussion of the challenges and limitations of predictive modeling in healthcare. The authors highlight the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. By addressing these challenges and limitations, the book enables readers to build predictive models that are both accurate and ethical. Finally, the book concludes by discussing future directions for predictive modeling in healthcare. The authors explain the use of artificial intelligence and big data analytics in healthcare and how these technologies can improve patient care and outcomes. With these insights into the future of predictive modeling in healthcare, readers will stay up-to-date on the latest developments in the field.
This volume is an essential resource for healthcare professionals, data scientists, and researchers interested in developing and implementing predictive models in healthcare. Included herein is practical guidance on developing predictive models, from data collection and preprocessing to algorithm selection, validation and evaluation, and applications. By emphasizing the importance of accuracy, reliability, and ethical considerations, the authors enable readers to develop predictive models that can improve patient outcomes and ultimately provide better patient care.
We thank all the participating authors who helped us tremendously with their contributions, time, critical thoughts, and suggestions to assemble this peer-reviewed, edited volume. The editors are also thankful to Scrivener Publishing and its team for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during this work.
Sandeep Kumar
Anuj Sharma
Navneet Kaur
Lokesh Pawar
Rohit Bajaj
November 2023
1Impact of Technology on Daily Food Habits and Their Effects on Health
Neha Tanwar1, Sandeep Kumar2*and Shilpa Choudhary3
1Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India
2Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vijayawada, India
3Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, India
Abstract
In this modern and busy lifestyle, we all look for ready-to-eat food. Food industries turn toward full automation to provide ready food nowadays. Prepared and packed food has an impact on health in the modern lifestyle with eating habits consumers seeking the technology viz. food diets application, online food delivery systems, and robotic food making machines. In this chapter, we have discussed the impacts of technology on daily food habits. The importance of technology in the food industry and its problems are highlighted in this chapter, with a focus on artificial intelligence, bioinformatics, 3D printing, sustainable applications of functional and nutraceutical food, and the need for a coordinated regulatory framework. The natural nutrients included in food, including carbs, proteins, vitamins, fats, antioxidants, and minerals, are necessary for the body parts to work normally physiologically. Achieving good health from sustainable food systems for the people is one of the most significant issues facing our world today. This chapter also focuses on different processed foods and their health impacts.
Keywords: Technology, food habits, artificial intelligence, digitization, emerging technologies
1.1 Introduction
We truly are what we eat, as the phrase goes. In other words, nutrition is essential to our health. Food provides information to our bodies, which also require ingredients to function properly. Our metabolic processes become disrupted and our health degrades if our body doesn’t receive the proper signals [1]. If we give our bodies non-healthy foods, our bodies get the wrong information, and we have to suffer many diseases. Several exciting pieces of evidence show that dietary factor plays a vital role in maintaining the systems and mechanisms of mental function. The relative abundance or scarcity of specific nutrients can affect cognitive processes. Cognitive ability is influenced by several gut hormones which can enter the brain, and these hormones function depending on the type of food intake. Although there are definite patterns, such as the need for nutrition balancing, there is no universally accepted definition of a healthy diet. Also, this relies on the features of every person and their surroundings [2]. -Gregorio Varela, Chairman of the Spanish Nutrition Association
Our food is different from what it was 20 years ago. The soil nutrients have been depleted, and chemicals are increasingly used to get more yield. Because of the growing quantity and variety of available food products, food choices are complex and vary over a short period, influenced by many factors like social, cultural, biological, psychological, and economic factors [3]. We have a lot of food variety and approx. Seventeen thousand new products are introduced each year. So we are heavily dependent on processed foods. The examples of food tech businesses include robotics, 3D food printing, alternative proteins, and individualised nutrition. Although these technologies have a tremendous positive impact on the food business, they merely touch the surface. These technological advancements and the internet era promote new food products that give fulfillment in less time.
1.1.1 Impacts of Food on Health
Food is central to our health. The food we have gives information and materials to our bodies that we need for the proper functioning of our bodies, as shown in Figure 1.1. This information can be right and wrong, depending on our food. To sustain, prevent, and treat disease, food serves as medication. The nutrients in food give all the necessary nourishing things and information by which our cells enable them to perform their functions. The metabolic processes slow down or occasionally even cease when the amount of nutrients consumed is not appropriate for the demands of the cell’s activity [4]. A healthy and balanced diet gives us plenty of energy to work, enjoy ourselves, and keep our immune systems healthy. The both science and art concerned with maintaining health and the prevention, relief, or cure of sickness, according to Webster. Nutrients come in a wide variety of forms, and we classify them into two groups: macronutrients and micronutrients, as shown in Figure 1.2.
Figure 1.1 Role of food habits on our mental health.
Figure 1.2 Macronutrients and micronutrients.
Macro (big) NutrientsWe need large amounts of carbohydrates, sugars, and dietary fiber from pieces of bread, beans, cereals and grains, pasta, fruits, and non-starchy vegetables. We obtain fats, fatty acids, and cholesterol from red palm oil, coconuts, groundnuts, soybeans, oily fish, avocados, butter, ghee, lard/cooking fat, whole milk, and cheese. We also obtain fats from meats and meat products (such as sausages) and fowl. There are many various types of proteins; some examples include those found in animal-based meals like meat, chicken, fish, eggs, and dairy products as well as those found in plant-based foods like pulses, fruits, and vegetables [
5
].
Micro (minor) NutrientsMinerals like iron, iodine, and zinc are among the micronutrients, or minor nutrients, which humans need in very small amounts yet are most often inadequate in our diets. Beef, liver, and other organ meats, poultry, fish, breast milk, as well as seaweed, legumes, almonds, and other foods provide us with these nutrients, vitamins, such as folate, vitamin B-group vitamins (which also contain vitamin A), and vitamin C [
6
].
1.1.2 Impact of Technology on Our Eating Habits
Technology changes every aspect of people’s lives and their communication, lifestyle, thinking, learning, and food habits. Food habits are changed with the rise of Internet of Things (IoT) and Artificial Intelligence (AI). Sharing food pictures on social media like WhatsApp, Facebook, Twitter, Instagram, etc., has grown globally [7]. Many people have even made their careers as food bloggers out of employing this trend on their feeds as shown in Figure 1.3. From every aspect, technology is changing our way of food habits. According to the Choosi Modern Food Trends Study, 50% of consumers get ideas for meals from others’ internet food photos. 39% of those surveyed stated that social media influenced their current eating habits.
Now, the question arises: How does technology affect our eating habits, and how will this change in the future?
1.2 Technologies, Foodies, and Consciousness
Technological influence may have both positive and negative effects. Figure 1.4 demonstrates that food is more than simply a necessity for survival.
Figure 1.3 Technological innovations in food sector.
Figure 1.4 Food on social media.
From one perspective, it increases our awareness of what we eat and current dietary trends, which develops better eating habits, at the same time, problematic internet users, uncontrollable craving habits, and eating disorders such as loss of control eating, binge eating disorders, etc. are increasing by the higher rate [8]. Problematic Internet Use (PIU) comprises passive behaviour brought on by excessive technology use as well as adverse social comparisons that may arise from exposure to and self-comparison with anything on their home feed. When it comes to teenagers, it becomes more distracting because of their undeveloped skills and the constant pressure they face through the internet world. It is important to understand online marketing and how it can be deceptive, as people can’t touch, feel or smell what’s advertised. Technology has improved accessibility—find, grab, and get. This on-demand culture has naturally shifted our food habits as well. Technology gives a faster way to get your food. Everybody likes ready to eat, ready to drink, and mull meal bars because it takes just a few minutes to prepare without effort. Technology does not affect our food habits as well as it affects the food industries [9].
According to the latest available statistics from the Australian Institute of Health and Welfare, which covered the years 2017 to 2018, 7.7% of adults and 17% of children were obese. As a result, one in four kids are at an elevated risk for physical health problems as well as greater mortality and sickness risks as adults. In order to prevent these tendencies from developing later in life, it is important to foster a positive link between food and technology from early childhood and adolescence on.
Technology has positive impacts also; like presently, so many intelligent appliances make cooking more accessible and less time-consuming, like smart cookers, electric inductions, ovens, etc. New technologies change everything from what we eat to how it to made by minimizing waste and environmental impacts. New automation raises high-skill jobs in the food sector and puts manual workers’ livelihoods at risk. So, the effect of technology is much more complicated [10]. On one side, it looks beneficial; on the other, so many detrimental effects also exist.
There are several major food technology trends in the limelight, for example, lab-grown meat, produced by culturing animal cells in vitro, and vegan beef, which is made from vegetarian ingredients. Plant milk is an alternative to milk, so many processed foods. This technological revolution and the internet’s growing effect have changed how we perceive food and eating. Some changes are of positive impact, while others are troublesome. We are living in 2023, where in this technology era, without stepping out of the house, everybody can enjoy their food at any time just by ordering online on different apps. Fast-food franchises have grown tremendously in the last some years. Yes, that’s what technology is doing to us: changing how we used to eat. With this technological era, the obesity epidemic is also on the scene [11]. Many factors influence the obesity epidemic, like reduced physical exercise due to more screen time, more caloric intake, easy access to fast food because of doorstep delivery, and as no one has time to make nutritious meals at home and customers want quick meal alternatives, the number of women entering the job is rising (not necessarily beneficial). The author [12] analysis that, there was a significant variation in the cost of food from 1950 to 2007, and it was discovered that the cost of fruits and vegetables climbed with time while the cost of snacking food reduced. For instance, the cost of a banana is currently around 5 Rs. The cost of chocolate is 1 Rs. concurrently. Here, we can directly link the consumption of calorie-dense foods to rising obesity rates over time.
1.3 Government Programs to Encourage Healthy Choices
With the help of large-scale awareness efforts like “Aaj Se Thoda Kam” and Trans-Fat Free India@75, the FSSAI hopes to eradicate fat by the year 2022.
To combat the widespread lack of micronutrients in the nation, food fortification is also actively marketed on a massive scale.
Eat Right India movement, 2018
Mid-Day Meal in Schools (MDMS) for children studying in a government school.
In accordance with the human life cycle concept, the National Food Security Act of 2013 provides for food and nutritional security.
Dietary guidelines of USDA Nutrition Education
1.4 Technology’s Impact on Our Food Consumption
Many studies have analyzed that eating if we are using technology (T.V., mobile, online games, etc.) impacts the amount of food we eat and the memory we retain of the consumption as shown in Figure 1.5. Nutritionists claim that technology diverts our attention during meals and may have an impact on how much food a person eats. It was discovered in a research of 119 individuals that when they played a straightforward computer game while eating, they consumed much less food than when they consumed the same meal without any interruptions. When they were preoccupied with technology, they had trouble accurately recalling how much they had been provided and consumed. Moreover, the University of Illinois at Urbana-Champaign examined the same study. Distraction also depends on what type of technology you use and what food is served [13, 14].
Figure 1.5 Impact of technology on our food consumption.
Technology makes our food more sustainable. Plant-based protein that gives taste and flavor to meat, foods for tomorrow, impossible foods, and beyond beef are the new players for more sustainable food options. Vegan products, alternative soy, pea, and potato protein attract vegetarians and meat eaters. There are so many innovative apps that care about what is eaten and what is left over. Too Good to use technology app helps eliminate food waste; they fill the gap between retailers with excess food and food waste warriors who are always looking for a real bargain. Due to the accessibility and simplicity of apps, awareness about food waste and its effect on the environment gained momentum in a few years [15].
1.5 Customized Food is the Future of Food
The future is about altering your meal to suit you, and food customizing is an encompassing concept, as shown in Figure 1.6.
It can be emotional and based on an individual’s choice. For example, customizing a food product externally or gifting name-branded chocolate or sweets leaves a memory long after the taste is gone. Customers can choose the content of their food item with a high degree of personalization as per their occasion. Subway is a great example where customers can customize their meals according to their taste or nutritional preferences [16].
Figure 1.6 Food customization.
1.6 Impact of Food Technology and Innovation on Nutrition and Health
Food industries use innovative and emerging techniques to develop products that offer variety, convenience, and health benefits as shown in Figure 1.7. These include less harmful alternatives to traditional protein sources, regional cuisines, nutraceuticals, and specialized nutrition [17]. Food industries are digitizing their production, management, and ecommerce area. However, many food products are more expensive than one can only afford.
Highly processed foods and minimally processed foods are in trending. Minimally processed food is rich in nutrients and reduces the risk of chronic health conditions. Yet, because they frequently include harmful amounts of sugar, salt, and fat, highly processed meals are generally not nutritionally similar to the foods they aim to replace. Dairy milk replacements, for instance, are better options but may not have the same nutritional value as dairy milk. New food items include plant-based substitutes, functional foods, replacement foods, and unique food products have seen a quick growth in popularity because to customer demand, food technology, and creativity [18]. Personal values, environmental sustainability, social justice, and animal welfare are all part of this movement. Food industries respond to consumer demands and the environment with the help of technology and innovations. With the use of technology, innovative food products are more convenient and add variety to eating patterns, and they can be a suitable replacement for traditional. For example, vermicelli made from rice, semolina, different flours, soybeans, lentils, or quinoa can be a suitable replacement for conventional that is made from maida. With a simple ingredient swap that does not alter the meal’s preparation or nutritional content, they add extra nutrients and fiber to the diet. Similarly, sugar is replaced by polyols, commonly known as sugar alcohols, and car-bohydrate-based fat substitutes, including carrageenan. A type of seaweed, starch-based gels, guar gum, maltodextrins, etc., replaces fats. And garlic salt, celery salt, potassium chloride, etc., are popular alternatives to table salt.
Figure 1.7 Impact of food technology and innovation on nutrition and health.
1.7 Top Prominent and Emerging Food Technology Trends
Grain-free flours: grain-free flours mean a step beyond gluten-free grains. Some people have an allergic reaction to gluten that can damage the small intestine, and some people avoid gluten for other reasons, like celiac disease, weight problems, etc. So, gluten-free flours are the healthier alternative; for example, almond flour, cassava flour, tiger nut flour, coconut flour, oat flour, gram flour, banana flour, arrowroot, tapioca, chestnut flour, sunflower seed flour, etc., as shown in
Figure 1.8
[
19
].
Plant-based Alternatives: Plant-based proteins are alternatives to animal proteins due to health and environmental concerns. Algae-based protein, fungi-based protein, The main sources of alternative protein are cultured meat, fermented proteins, lab-grown food, plant-based nutrition, edible insect proteins, and mycoprotein [
20
]. Blue Tribe Foods is India’s first plant-based meat company. Similarly, Beyond Meat, Imagine Meats, GREENEST, Shaka Harry, etc., are some Indian startups in this field. The Protein Brewery (Dutch Startup) develops protein, an animal-free lab-grown food made from some non-allergenic crops, fungi, essential amino acids, and fiber, as shown in
Figure 1.9
. Ento (Malaysian startup) develops insect-based proteins etc.
High-quality plants are increased exponentially to reduce meat consumption; for example, black bean quinoa veggie burgers are crave-able meat alternatives, as faux sausage crumbles and garlic and fennel plant-based sausages. Plant-based patties that look and taste like beef, good catch fishfree tuna, and meatless crumbles, as shown in Figure 1.10.
Functional Foods and Ingredients: Functional foods provide additional health benefits to the consumer beyond essential nutrition. Fortification and available ingredients are bioactive compounds that can be used in manufacturing food ingredients, e.g., stanol, sterol, or dietary fiber [
21
]. Every component of food, including functional baby food, functional bakery and confectionary goods, functional dairy and dairy-based products, functional drinks, etc., has a growing market for functional foods, as illustrated in
Figure 1.11
.
Figure 1.8 Different types of gluten-free grains.
Figure 1.9 Alternative protein market map.
Figure 1.10 Plant-based meat and protein substitutes.
Figure 1.11 Market size of functional food ingredients.
Personalized Nutrition: Depending on the preferences of each consumer, it may offer nutrigenomics-based diet options, vegan diets, sugar- and gluten-free options, and clean-label food goods.
Figure 1.12
illustrates how 3D printing and robots in food assembly lines enable food makers to scale up nutrition customization.
Personalized nutrition uses massive data and machine learning approaches to include vast information on consumers’ medical history, age, sex, allergic foods, etc. to improve consumer health [22]. For example, with the help of continuous glucose monitoring, gut health tests, etc., we can critically analyze our health system and personalize our diet. NGX (British Startup) develops genetically personalized meal shakes, Anrich 3D (Singaporean Startup) provides personalized nutrition using 3D printing, SUPP nutrition (an Indian startup) includes mineral and vitamin supplements, etc.
Figure 1.12 Personalized food as per individuals.
Digital food management: Digital food management is a vital part of food management that directly connects the food industries and consumer (D2C) distribution as shown in
Figure 1.13
. This digital food management system uses artificial intelligence-powered WiFi access points that self-regulate consumers’ behavior and then offer personalized deals to build trust. BaroBite (a Canadian startup) automates marketing for restaurants, bars, and lounges with a social media platform where customers can connect through posts and stories [
23
].
Figure 1.13 Digital food management.
Ecommerce: Pandemic situation pushed innovations in food supply chains, and ecommerce has become the spotlight in the food industry, as shown in
Figure 1.14
. Online distribution services are available on request using digital channels to reach clients, which works on a direct-to-customer (D2C) model. Swiggy and Zomato to order food come under in the top 10 e-commerce food delivery companies [
24
].
3D Food Printers: 3D Food printers are an advanced manufacturing technique, using laser, bioprinting, food printing, etc., to develop food products, as shown in
Figure 1.15
. It offers personalized food products and diets at a large scale without additional operating costs. With the help of food printing, food can be customized in any shape, color, size, flavor, nutrition, or texture, making it very attractive, appealing, and valuable for various fields like robotics. Fab@Home, pasta printer, Foodini, CandyFab, etc. These are some examples of 3D food printers.
Robotics: The food industries, food brands, and beverages incorporate robotics in their entire chain to get more consistency, effectiveness, and better hospitality to improve customer convenience and safety. Drones and vehicles are emerging technology in the food sector chain that gives more cost-effective, fast, and better monitoring service, as shown in
Figure 1.16
.
Overall, the food business is making more money thanks to robotics thanks to improved speed and precise quality control. Servi, a food service robot that assists waiters with carrying plates, was developed by the Bear Robotics business [25]. With robotics solutions, hospitality has become more exciting and more manageable. Similarly, ROBOEATZ provides AI-based autonomous robotic kitchens for restaurants, 5-star hotels, etc.
Figure 1.14 E-commerce platforms.
Figure 1.15 3D food printing.
Figure 1.16 Robotics in food industry.
Food Waste Reduction: Earlier, a massive part of the food was wasted, so food industries and entrepreneurs focused on reducing food wastage. Food monitoring and reusing solutions allow for reduced food waste. Innovative packaging also plays a significant role in reducing food wastage. For example, bar codes, intelligent labels, real-time data tracking, and time–temperature indicator are some innovative techniques to know all about your food, like when your food is packed, how much freshness is there, in how many days it will spoil by changing code color, etc. that prevent waste between from farm to fork. RFID technology, Radio Frequency Identification, uses the electromagnetic field to track and identify objects automatically [
26
]. It plays a vital role in waste management, adding the capability to sense when it is time to pick up the trash. 3D food printing uses food waste to print edible food products.
Food safety and transparency: Customers are becoming more concerned with food product quality and safety than actual purchases. As shown in
Figure 1.17
, using smart labeling and independent food grading equipment, consumers may obtain product information prior to making a purchase.
To win customers’ trust via food quality and safety, food safety, and transparency are crucial. Both the Canadian business ThisFish and the French startup Quality provide software-as-a-service (SaaS) to automate food safety.
Nonthermal Techniques: Nonthermal techniques mean processing foods without any heat treatment that can maintain their quality and nutritional factors and eliminate microbial reduction. High-pressure processing, irradiation, cold plasma, pulsed electric field, etc., are some nonthermal processing techniques [
27
,
28
].
Figure 1.17 Food transparency with clean food labels.
1.8 Discussion
However, some disadvantages of this technology should be addressed, and one of the most remarkable disadvantages is that processing removes nutrients. Now, all over the world, supermarkets are full of convenient processed food items that appeal to our taste buds, but food processing can strip many nutrients while refining the process present in fresh food. So, we need to get them from elsewhere.
These processed foods also include artificial color, additives, flavoring agents, and chemically altered fats and sweeteners, which can give wrong signals to our bodies and cause health problems.
Processed food can be used for convenience and speed, not for the pleasure that we used to get from natural homemade food.
Reduced physical activity creates health issues.
1.9 Conclusions
Technology in the food industry has advanced significantly in recent years, and as a result, consumer food tastes are changing dramatically. The popularity of online shopping applications is altering consumer behavior, and the level of interaction and personalization with customers is rising. With the use of clever tags and codes, technology has improved transparency and traceability. Robots also aids in lowering food waste. In addition to the benefits, there are also drawbacks, such as a complete reliance on technology, eating problems, the use of chemicals and preservatives, the absence of physical activity, etc. Technology can help us change our eating habits and address a number of current issues.
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Note
*
Corresponding author
:
2Issues in Healthcare and the Role of Machine Learning in Healthcare
Nidhika Chauhan1*, Navneet Kaur2, Kamaljit Singh Saini2and Manjot Kaur3
1I UIC Department, Chandigarh University, Punjab, Mohali, India
2Department of CSE, Chandigarh University, Punjab, Mohali, India
3Fidelity Information Services Ltd., Mohali, India
Abstract
The healthcare industry is one of the world’s most significant and rapidly growing sectors. As a result, healthcare administration is transitioning from conventional methods to digital ones. The transition phase of the healthcare industry is confronting several issues like as the number of medical cases is increasing, the data are also growing. This information could comprise crucial details about the patient’s medical background, physicians’ recommended treatments, medical examination outcomes, etc. All these data are enormous, complex, and diversified; along with that, this data also faces issues like privacy, security, data hacking, data management, etc. To overcome these challenges, machine learning (ML) tools are used for data analysis, prediction, and classification. It is used in healthcare to classify the disease more accurately and overcome the challenges of multiple outcome optimization or sequential decision-making issues. The chapter aims to study current healthcare systems, various healthcare issues, several factors that affect healthcare, and how machine learning is employed to overcome these challenges. The role of machine learning in healthcare is critically studied.
Keywords: Healthcare, issues in healthcare, factors affecting healthcare, machine learning
2.1 Introduction
The healthcare concept describes a system that improves health-related facilities to cater to patients’ clinical needs. Doctors, practitioners, healthcare professionals, researchers, and healthcare industries are all working hard to maintain and improve healthcare services and preserve medical records. With the effective delivery of the technology over the years, information is continuously growing throughout every industry, along with healthcare, which would, in turn, requires an increasing number of data mining techniques [1–5].
Public healthcare information systems often called clinical informatics, describe the application of data design and integration to the context of biomedical practice, comprising the management and use of individual healthcare information. It employs a holistic strategy for health information to enhance healthcare by focusing on more modern prospects. Essentially, it affects the advancement of data acquisition, storage, recovery, and utilization in medicine and biomedicine [6–9]. However, as the medical system digitizes, medical institutions generate vast clinical information [10–15]. Generally, medical datasets refer to all health-related documents that are digitally recorded [13, 16–23]. It might include extensive details on the patient’s medical history, doctor’s recommended instructions, medical tests, etc. All this information is massive, multi-dimensional, and diverse. Due to the increasing difficulty of health information, making wise decisions is difficult today [12, 23–38].
Advanced healthcare information systems broaden the scope of primary healthcare facilities by including elements ranging from advanced techniques to computational engineering. Efficient content analysis improves overall operations by considering every aspect. Public healthcare analytics integrates digital technologies, research, and health disciplines to establish a more smooth and more efficient management process that benefits individuals globally [38–44]. The primary goal of medical care bioinformatics is to deliver better patient services by leveraging technological developments in global health, clinical trials, pharmacy, and other fields. Unfortunately, there needs to be more aware of the analytical techniques that could be extremely useful for the healthcare industry and its treatment of patients around the globe [42]. Belle et al. [45–51] explored different smart healthcare technology problems that may be solved with analytical modelling. Due to escalating expenditures in the medical sector in countries such as the U.S., big data analytics has become essential in this domain [52–62]. Furthermore, prices are far more excellent than they should be and have been growing for the past 20 years. We require innovative, data-driven reforms in the healthcare industry. Machine learning, data mining, and statistical approaches are significant areas of research that boost persons’ capacity to make appropriate decisions to optimize the performance of any professional sector [55, 63–71]. Compared to the quantity of data generated, the scale of human data analysis ability is substantially lower [71–73]. This is especially important in the healthcare industry, where the extent of qualified professionals for medical data analysis could be much more significant.
In this chapter, we aim to discuss the healthcare system, issues in healthcare, factors affecting healthcare, and the role of machine learning in healthcare. The chapter is structured into various sections to cover the mentioned topics. An overview of issues in healthcare is given in the subsection. The following section discusses factors affecting healthcare. The chapter further discusses machine learning in healthcare, followed by a conclusion.
2.2 Issues in Healthcare
The medical field is one of the world’s most significant and rapidly growing industries. Consequently, healthcare administration is shifting from conventional to digital methods [72]. With this transformation, the healthcare industry faces various challenges mentioned in Figure 2.1:
Figure 2.1 Issues in healthcare.
2.2.1 Increase in Volume of Data
The medical industry is undergoing a radical transformation. Due to the modernization of healthcare, a tremendous volume of medical data is being generated. Information Technology in healthcare has progressed to the point that it can gather, maintain, and send information digitally from any part of the globe in real time. Nowadays, all health-related documents are digitally preserved. Each one of these datasets is massive, multidimensional, and diverse in character, resulting in big healthcare data [6–9]. Such data may be gathered from various internal and external sources. This data includes clinical information, biometric data, picture records, social media data, etc. This exponential growth and diverse nature of healthcare data have become one of the most significant concerns as it becomes difficult to manage and store due to its heterogeneity and large size [10–16].
As healthcare data has increased rapidly, it has generated various issues, constraints, and complications. These issues are further discussed that must be addressed [3, 11].
2.2.1.1 Data Management
Information management is a significant concern in healthcare data because it is obtained in various forms; it must be validated and processed.
