Disease Prediction using Machine Learning, Deep Learning and Data Analytics E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2024
This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes.
The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases.
Key topics:
- Federated learning in analysis of sensitive healthcare data while preserving privacy and security.
- Artificial intelligence for 3-D bone image reconstruction.
- Detection of disease severity and creating personalized treatment plans using machine learning and software tools
- Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases
- Brain-computer interfaces
- Data mining for standardized electronic health records
- Data collection, management, and analysis in epidemiological research
The book is a resource for learners and professionals in healthcare service training programs and health administration departments.
Readership
Learners and professionals in healthcare service training programs and health administration departments.
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FOREWORD
It is my pleasure to write the foreword for the book titled “Disease Prediction using Machine Learning, Deep Learning and Data Analytics”. The book covers the role of machine learning in boosting the immunity of a person, role of federated learning in healthcare, role of data mining in developing a medical support system, and role of AI in establishing interaction between human brain and computer.
This book covers the detection of diabetic retinopathy using machine learning algorithms, deep learning based model for conversion of 2-D images into to 3-D images, developing a decision support system for prediction of asthma attack, early prediction of eye diseases, computer-aided bio-medical tools for disease identification, deep learning based systems for medical data classification and AI-based chatbot system for healthcare industry.
The book “Disease Prediction using Machine Learning, Deep Learning and Data Analytics”, gives a clear idea about the deep learning techniques employed for analysis and classification of imagery data. The data mining algorithms for knowledge extraction and feature extraction techniques attract the readers working in the field of medical image analysis. The book provides the mechanisms involved in designing and developing the clinical decision support systems.
“Disease Prediction using Machine Learning, Deep Learning and Data Analytics”, is a must read book for the academicians, researchers and students working in the field of applications of machine learning and deep learning for disease diagnosis and prognosis. The book is important to read for the clinical experts who are keen to adopt the techno-tools as assistants for diagnosis and prognosis of diseases.
I would like to congratulate the Editor in Chief, Dr. Geeta Rani and Associate Editors, Dr. Pradeep Kumar Tiwari and Dr. Vijaypal Singh Dhaka for bringing the ideas of academicians and research community together at a single platform. I strongly believe that their expertise in the field of machine learning, cloud computing and medical image analysis will be effective in attracting the readers in the field.
PREFACE
In the recent era, the use of data analytics and machine learning algorithms has been observed in the arena of the medical field. Literature shows the successful application of data analytics and machine learning techniques for making predictions using real-time data collected from medical fields. The efficacy of machine learning models in image processing, big data analytics, object detection, automatic extraction, and tailoring of features is a great motivation for employing these models in the medical field. A boom in the use of machine learning and deep learning models is observed since the last decade. These models automatically extract the features from medical images, identify the most prominent features and predict diseases such as pneumonia, COVID-19, emphysema, lung tuberculosis, tumor, etc. can be predicted by training the deep learning model with chest radiographs and CT scans. These models not only predict the disease but are also useful in visualizing the infection in the organs. For reliable prediction, there is a need to design the custom architecture of the model. The architecture designer must focus on the size of the dataset, versatility, and quality of the dataset, types and number of predictions to be provided. The architecture is also dependent on the type of analysis required for disease prediction.
Literature reveals a lot of information about the design of methods for disease prediction.
But, poor availability of systematic information at one source becomes challenging for the students, academicians as well as researchers working in this field. Researchers face problems in identifying suitable algorithms for pre-processing, transformations, and integration of clinical data. They also seek different ways to build models, and prepare data sets for training and evaluating the models. Moreover, it becomes significant for them, to observe the impact of decision-making strategies on the accuracy and precision of the predictive models designed on the basis of techniques such as Logistic Regression, Neural Networks, Decision Trees, and Nearest Neighbors. Thus, there is a strong need of providing well-organized study material with practical aspects and validation. The book smartly fills the gaps.
This book invited ideas, proposals, review articles and experimental works from the researchers working in the field. The systematic organization of the research works in the field of applying machine learning for disease prediction will be fruitful in providing insights to readers about the existing works and the gaps available in the field. This book is a significant contribution towards providing a detailed study of data analytics algorithms and machine learning techniques for disease prediction. The book includes a rigorous review of related literature, methodology for data set preparation, model building, training, and testing the model. It contains a comparative analysis of versatile algorithms applied for making predictions in the challenging arena of medical science and disease prediction. The provides good insight into the topics such as Data Analytics, Machine Learning, Deep Learning, Information Retrieval from medical data, Data Integration, Prediction Models, Medical Data Analysis, Medical Decision Support systems, Federated Learning in Healthcare, and Medical Image Reconstruction.
The book is a companion and a must-read, for academicians, people from industries, graduate and post-graduate students, researchers, physicians and for everyone who is involved in the fields of medicine, data analytics or machine learning directly or indirectly. The book is compiled in such a way that each chapter is sufficient to give a complete study set from problem formulation to its solutions. All chapters are independent of each other and can be studied individually without consulting other chapters.
Each chapter starts with an abstract, important key terms, and an introduction to the topic. It is followed by related works, challenges identified, methodology, and experimental results. The chapter ends with the concluding remarks and future directions.
This book includes chapters in the following research areas:
Review in the fields of Data Analytics, Machine Learning, and Medical Data Analysis.Federated Learning in Healthcare.3-Dimensional Image Reconstruction.Applications and Practical Systems for Healthcare.Information Retrieval from medical data.Data Integration.Prediction Models.Clinical Decision Support Systems.Computer-Aided Diagnosis.Mobile Imaging for Biomedical Applications.A brief summary of book chapters is given below:
Chapter 1: Role of Federated Learning in Healthcare: A Review
In this chapter, the authors provide a detailed comparative study of the different deep learning-based models employed in federated learning. They discussed how efficiently the model can classify chest radiographs into Covid-19, pneumonia, and normal categories. This chapter provides the benchmarking information and analysis for the researchers looking forward to developing deep learning-based applications of federated learning in healthcare.
Chapter 2: Role of Artificial Intelligence in 3-D Bone Image Reconstruction: A Review
This chapter presents a review of the bone imaging techniques and techniques applied for the conversion of two-dimensional images into three-dimensional form. It also gives directions for developing patient-specific and organ-specific optimized techniques for 3-D reconstruction.
Chapter 3: Role of Machine Learning and Deep Learning Techniques in Detection of Disease Severity: A Survey
This chapter explores the role of machine learning and deep learning techniques in the detection of disease severity. It presents a survey of the latest methodologies and algorithms employed in analyzing medical data to predict and assess the severity of various diseases, empowering clinicians with valuable insights for personalized treatment plans. The chapter highlights the advantages and drawbacks of different ML and DL techniques employed for prediction of disease severity.
Chapter 4: Computer-Aided Bio-Medical Tools for Disease Identification
This chapter investigates computer-aided biomedical tools for disease identification. It discusses the development and utilization of innovative software tools and techniques that assist in the identification and diagnosis of diseases, augmenting healthcare professionals' decision-making process. The chapter highlights the importance of computer-aided bio-medical tools as techno-assistants for health experts.
Chapter 5: Prognosis of Dementia using Machine Learning
In this chapter, the authors discuss the prognosis of dementia using machine learning. They explore the potential of machine learning algorithms in predicting the progression and prognosis of dementia, offering valuable insights for early interventions and personalized care plans.
Chapter 6: A Clinical Decision Support System for Effective Identification of Onset of Asthma Disease
This chapter presents a clinical decision support system for the identification of asthmatics in two different cohorts representing rural and urban populations in India. It provides details about developing a hybrid decision support system by uniquely combining unsupervised and supervised learning techniques.
Chapter 7: Applying Deep Learning and Computer Vision for Early Diagnosis of Eye Diseases
This chapter presents a study to raise awareness about various eye disorders. It provides a discussion on the role of computer vision, image processing, and deep learning techniques in the early diagnosis of the disease. Thus, it may prove useful for enhancing early disease treatment and minimizing the chances of blindness.
Chapter 8: The Fusion of Human-Computer Interaction and Artificial Intelligence Leads to the Emergence of Brain Computer Interaction
In this chapter, the authors discuss the Components Brain Computer Interface, its characteristics and challenges. They provide details of how conventional classifiers are replaced with Convolutional Neural Networks (CNNs). The chapter also reveals that the EEG signals from the brain can be linked seamlessly to mechanical systems via BCI applications, making it a rapidly growing technology. The presented technology has applications in the fields such as Artificial Intelligence and Computational Intelligence.
Chapter 9: Mining Standardized EHR Data: Exploration, Issues, and Solution
This chapter focuses on mining standardized Electronic Health Records (EHR) data, providing an in-depth exploration. This chapter examines the process of extracting knowledge and insights from standardized EHR data through data mining techniques. It explores the challenges and opportunities associated with mining EHR data, including data quality issues, data integration challenges, and ethical considerations of handling sensitive patient information. Additionally, the chapter presents innovative solutions and methodologies for effectively mining EHR data to support various healthcare applications such as clinical decision-making, predictive analytics, and population health management.
Chapter 10: Role of Database in Epidemiological Situation
This chapter presents the crucial role of databases in epidemiological situations. It highlights the significance of databases in epidemiological research, providing a comprehensive overview of their role in data collection, management, and analysis. In this chapter, the authors explore different types of databases commonly used in epidemiology, including disease surveillance systems. Moreover, the chapter discusses the challenges and considerations associated with database implementation, such as data standardization, and privacy protection.
INTRODUCTION
The healthcare industry is undergoing a transformative phase, driven by technological advancements and data-driven solutions. The integration of cutting-edge technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), have opened new horizons for improved healthcare delivery, disease diagnosis, and patient care. Additionally, the utilization of computer-aided tools helps clinicians and researchers to make more accurate and timely decisions. This motivated us to provide a concise information regarding technology-based solutions in healthcare.
This book aims to explore and discuss the significant role of technology and data in various domains of healthcare. It delves into the latest research and developments in the field, providing comprehensive reviews, surveys, and case studies on topics ranging from federated learning to disease prognosis and biomedical tools. It also delves into two important aspects of leveraging technology and data in healthcare, mining standardized EHR data and the role of databases in epidemiological situations.
Each chapter focuses on a specific aspect, highlighting the potential impact of technology on enhancing healthcare practices and outcomes. The brief outline of each chapter is given below:
Chapter 1 examines the role of federated learning, a distributed machine learning approach that enables collaborative analysis of sensitive healthcare data while preserving privacy and security. It presents a comprehensive review of the applications and benefits of federated learning in healthcare settings.
In Chapter 2, the focus shifts to the role of artificial intelligence in a 3-D bone image reconstruction. This chapter provides an in-depth review of the advancements in AI techniques for reconstructing detailed and accurate three-dimensional bone images, aiding in better diagnosis and treatment planning.
Chapter 3 explores the role of machine learning and deep learning techniques in the detection of disease severity. It presents a survey of the latest methodologies and algorithms employed in analyzing medical data to predict and assess the severity of various diseases, empowering clinicians with valuable insights for personalized treatment plans.
Chapter 4 investigates computer-aided biomedical tools for disease identification. It discusses the development and utilization of innovative software tools and techniques that assist in the identification and diagnosis of diseases, augmenting healthcare professionals' decision-making process.
In Chapter 5, the authors discuss the prognosis of dementia using machine learning. This chapter explores the potential of machine learning algorithms in predicting the progression and prognosis of dementia, offering valuable insights for early interventions and personalized care plans.
Chapter 6 introduces a clinical decision support system for the effective identification of the onset of asthma disease. It explores how advanced technologies, including machine learning and data analytics, can be integrated into clinical workflows to enhance the early detection and management of asthma.
Chapter 7 delves into the application of deep learning and computer vision for early diagnosis of eye diseases. It discusses the utilization of these technologies to analyze medical images, enabling early detection and intervention in conditions such as diabetic retinopathy and glaucoma.
Chapter 8 explores the fusion of human-computer interaction and artificial intelligence, leading to the emergence of brain-computer interaction. It delves into the advancements in this interdisciplinary field, highlighting its potential to revolutionize healthcare through direct communication between the brain and computer.
Chapter 9 focuses on mining standardized Electronic Health Records (EHR) data, providing an in-depth exploration. This chapter examines the process of extracting knowledge and insights from standardized EHR data through data mining techniques. It explores the challenges and opportunities associated with mining EHR data, including data quality issues, data integration challenges, and ethical considerations of handling sensitive patient information. Additionally, the chapter presents innovative solutions and methodologies for effectively mining EHR data to support various healthcare applications such as clinical decision-making, predictive analytics, and population health management.
Chapter 10 delves into the crucial role of databases in epidemiological situations. This chapter highlights the significance of databases in epidemiological research, providing a comprehensive overview of their role in data collection, management, and analysis. It explores different types of databases commonly used in epidemiology, including disease surveillance systems. Moreover, the chapter discusses the challenges and considerations associated with database implementation, such as data standardization, and privacy protection.
In this book, we aim to discuss the advancements and potential of technology and data-driven approaches in healthcare.
DEDICATION
This book is dedicated to all authors who contributed their valuable work in this book. Further, the book is dedicated to everyone who supported the editors Dr. Geeta Rani, Dr. Pradeep Kumar Tiwari and Dr. Vijaypal Singh Dhaka directly or indirectly in completion of this book well in time.
List of Contributors
Role of Federated Learning in Healthcare: A Review
Geeta Rani3,Meet Oza1,Heta Patel1,Vijaypal Singh Dhaka3,*,Sushma Hans2Abstract
In the modern era, there is a boom in automating medical diagnosis by adopting emerging technologies and advanced applications of artificial intelligence. These technologies require a huge amount of data for training the models and precisely predicting the disease or disorder. Multiple organizations can contribute data for such systems but maintaining data privacy while sharing the data is a major challenge. Also, provisioning a large data corpus for the performance improvement of machine learning and deep learning models in the healthcare domain while keeping the patient’s medical confidentiality intact is a point of concern. Thus, there is a strong need to preserve the privacy of medical data. This calls for the use of up-to-the-minute technologies where the necessity of sharing raw data is completely eradicated, while each organization receives a catered infrastructure for processing data. A cross-silo federated learning model is based on the concept of decentralized data weights collection from multiple clients which are then processed on the central server for modeling and aggregation, thus maintaining data privacy in its true sense. The authors in this manuscript provide a detailed comparative study of the different deep learning-based models in federated learning and how efficiently they can classify lung X-Ray images into three classes: Covid-19, Pneumonia, and Normal. This study can provide a benchmark for the researchers looking forward to deep learning-based model applications of cross-silo federated learning in healthcare.
INTRODUCTION
When Covid-19 pandemic hit the world, it became very important to figure out various ways to detect the existence of the novel virus, apart from the usual RT-PCR tests. Scientists and researchers around the globe have studied, developed, and presented numerous ways to detect the novel Covid-19 virus. Many researchers have also provided multiple ways to detect Covid-19 and pneumonia through CT-Scans and X-rays of lungs with the use of machine learning techniques. But the accurate prediction via these machine learning and deep learning models for detection requires a large amount of dataset for training the models. In real scenarios, such large datasets are either not feasible due to system constraints or the patient’s medical confidentiality gets impaired. The feasibility issues persist even though we have access to many image annotation tools available in the market. This is because such tools and services are very expensive, and they also require expert supervision and proficiency especially if they are utilized for disease diagnosis. Along with monetary hindrances, the feasibility issue also refers to the communication overhead that would occur due to a large dataset being transmitted for centralization [1]. Such an issue exists in the healthcare domain and across all domains where the models are required to learn and train with the help of multiple clients’ data without invading the users’ privacy.
The application of traditional machine learning and deep learning in the field of healthcare has already been studied by various researchers for tumor prediction [2], covid-19 screening [3, 4], disease prediction [5], cardiovascular diseases prediction [6], coronary artery disease prediction [7], diabetes prediction [8], glaucoma detection [9], etc. A similar case pertaining to users’ privacy was encountered by Google in 2016 when the team coined the term ‘Federated Learning’ while advocating an advanced and novel approach that utilizes distributed data from mobile devices for training. Further, this approach presents how a central model is updated by only using the aggregate of the parameters of the local mobile devices [10]. Federated learning inherently trains the central model based only on the parameters passed on by local machine learning models. In addition to only sharing the parameters, the parameters are also encrypted before being passed on which increases data privacy. Federated learning can be differentiated from distributed learning because of the fact that the main objective of federated learning is training on a large dataset from different clients without the transfer of raw data. Whereas distributed learning focuses on distributing the computing resources across clients [11]. Incorporation of this Federated Learning along with cross-silo transferred learning opens new doors to endless possibilities of more accurate innovations due to the availability of a huge data corpus for training without actually having to exchange or transfer the data. In cross-silo federated learning, data is segregated as silos, i.e., multiple confined data sources which in turn centrally aggregate and train a model by passing out only the trained weights and parameters from each client. For collaboration between institutions of healthcare, finance, etc. user data is extremely sensitive, and open alliances might expose such sensitive data to various vulnerabilities. Cross-silo federated learning only sanctions weights and parameter transfer and hence the data fenced within the silos itself. And therefore, cross-silo federated learning serves as a superior alternative to the traditional centralized machine learning approaches. Federated learning has been so far applied to various healthcare applications. For example [12], distributed learning has been used to solve a problem related to hospitalizations due to cardiac cases; and [13] leveraged machine learning in a federated setting to predict fatality and duration of stay at the hospital using electronic medical records.
Federated learning systems seem promising but due to their nature of a dispersed framework, it faces certain challenges too such as, communication cost, resource cost, security of communication, etc. High communication costs refer to the overhead incurred due to ample transmissions of parameters for the training process. Frequent transfer of parameters is required between silos in order to present potent results and hence this communication overhead acts as a hindrance to federated learning especially when the connection is slow, and a considerable number of devices or organizations are involved. Also, since FL works on distributed systems, each system involved might have different computational power, different storage capabilities, and different bandwidths. A single slightly less efficient system can be a weak link to the entire process and on the other hand, providing all the concerned organizations with full-fledged resources might increase the system cost. Apart from these overhead challenges, privacy concerns also prevail in federated learning. Although federated learning is known as a mechanism to preserve user data privacy, it is not general wisdom that FL by itself doesn’t protect data privacy. Recent studies [14] have revealed that as models communicate constantly for the transfer of parameters, the process is seen to be leaking some information in the course. For example, [15] a study showed even a small section of original gradients may be enough to let local data slip from the system. Moreover, since the parameters are obtained via model training at the local level, vulnerabilities such as model inversion or attacks on model parameters can corrupt aggregate inference.
Even after weighing the opportunities and hindrances presented by the federated learning approach in healthcare, we can conclude that federated learning still races way ahead of traditional machine learning practices. The user data confidentiality issues that come along with the traditional approaches directly make the patients’ data susceptible. In this research, we propose a federated learning model to classify chest X-rays as COVID-19, Pneumonia, and normal lungs.
LITERATURE REVIEW
In this section, let us take a look at the previous works by researchers in this domain of federated learning to detect the abnormalities of lungs. Many researchers have experimented with federated learning to provide prominent techniques to detect the covid-19 virus in human lungs. Most of which accommodate CT-Scans as input data. For example, a group of researchers [16] Qi Dou, et al., presented a deep convolutional neural network-based artificial intelligence model using CT scans that incorporated federated learning to detect COVID-19 lung abnormalities. Here the main dataset was compiled from 3 hospitals in Hong Kong with 75 confirmed covid-19 patients. For external validation, the researchers used 4 other datasets which included 22 patients from China and 35 patients from Germany in total. The effectiveness of federated learning here was checked on full CT slices, i.e. without previously knowing if tissue damage is present or not. For every client in this experiment, the central aggregating server was given the dataset size that was given as input to every local device. Along with this, the central server was also provided with the weighted average of the local models for the updation of the global model on the central server. The model trained on an internal dataset showed the highest AUC score of 95.40% mostly because it had the single largest dataset. However, the joint model that was trained with all the internal datasets derived an AUC score of 92.97%. Along with the federated learning model, the researchers here applied ensemble learning over three models corresponding to the three datasets. The comparison clearly showed that the performance of federated learning across all metrics was superior to that of the model ensemble method. The finding from this research was that in order to curtail the false-positive predictions, transfer learning from an extensive dataset will be more suitable. For evaluation, although multiple sources of data were considered, it still included data of 132 patients only, which might induce model bias.
Similarly, another group of researchers [17], Dong Yang, et al., experimented by consolidating federated learning and semi-supervised learning. Here, the dataset consisted of COVID-19 data that included 736 CT scans of 700 patients from China; 496 scans of 244 patients from Japan; 472 scans of 147 patients from Italy. Other data included 38 CT scans of 38 patients from the National Institutes of Health. The CT scans of these patients were examined for known non-COVID-19 types of pneumonia from bacteria, and fungi, and non-COVID viruses were included as “other pneumonia”. The dataset also included 101 CT scan images of 101 patients, who were men diagnosed with prostate cancer, and 474 CT scans from the LIDC public dataset belonging to 474 patients. The study shows that the framework is efficient to extract valuable information only if unlabelled data is input by the clients and also demonstrates that a lower learning rate on unsupervised clients generally benefits all clients involved in the federated learning process. The researchers have shown that for the identification of COVID-19-infected regions, the data of patients with a completely COVID-19- free background also turned out to be contributive. This was achieved via 'false alarm rejection'. Although the model fails to classify other types of lung abnormalities like pneumonia or cancer, the dataset has high variation in demographics and so the accuracy metrics range approximately between 50-60%.
Further, Rajesh Kumar, et al. [18
