Human Cancer Diagnosis and Detection Using Exascale Computing E-Book

• Herausgeber: John Wiley & Sons
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• Sprache: Englisch

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Veröffentlichungsjahr: 2024

Beschreibung

Human Cancer Diagnosis and Detection Using Exascale Computing The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology. Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer. The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer. Audience This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.

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Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Human Cancer Diagnosis and Detection Using Exascale Computing

Edited by

Kapil Joshi

Department of Computer Science & Engineering, Uttaranchal Institute of Technology, Dehradun, India

and

Somil Kumar Gupta

School of Computing, DIT University, Dehradun, India

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-19767-5

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

The treatment of cancer has remained a top priority in the vast field of medical science and technology. Successful diagnosis and treatment of cancer is one of the major challenges of medical science. Millions of people throughout the world have been affected by cancer, and in many aspects. To deal with this challenge, the science and technology community has developed many cutting-edge mechanisms over time that have improved and developed new methods of cancer diagnosis and detection.

Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer.

This book is a comprehensive exploration of this transformative unification of science, medicine, and technology. In the pages that follow, we delve deeply into the realm of exascale computing and its profound implications for cancer research and patient care.

This volume serves as a bridge between the worlds of computational science and clinical oncology. It joins experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. Authors of different chapters within this book have given significant insight into the challenges posed by cancer and the innovative solutions that exascale computing offers.

The chapters that follow cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. We explore the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase.

Our goal in presenting this book is not only to educate and inform, but also to inspire. We aim to empower researchers, clinicians, and technologists with the knowledge and tools needed to advance the cause of cancer diagnosis and detection. By leveraging the unprecedented computational capabilities of exascale computing, we have the opportunity to accelerate discoveries, optimize treatments, and ultimately save lives.

We acknowledge that our understanding of cancer is still evolving, and the challenges ahead are formidable. However, with the synergy of human intellect and exascale computing, we have the potential to make profound strides in the battle against this devastating disease. Together, we can transform the future of cancer diagnosis and detection, offering hope to countless individuals and their families.

We invite you to delve into the pages herein and join us on this remarkable expedition at the intersection of science, technology, and medicine. Together, we can illuminate new pathways in the fight against cancer and usher in an era of hope, healing, and discovery.

We are deeply grateful to all authors for their excellent contributions to this book. We also highly appreciate the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during the publication of this book.

1Evaluating the Impact of Healthcare 4.0 on the Performance of Hospitals

Pramod Kumar1, Nitu Maurya2, Keerthiraj3, Somanchi Hari Krishna4*, Geetha Manoharan5 and Anupama Bharti6

1Faculty of Commerce and Management, Assam Down Town University, Sankar Madhab Path, Gandhi Nagar, Panikhaiti, Guwahati, Assam, India

2Management, KCC Institute of Legal and Higher Education, Noida, India

3Political Science, School of Liberal Arts, Alliance University, Bengaluru, Karnataka, India

4Department of Business Management, Vignana Bharathi Institute of Technology, Aushapur Village Ghatkesar Mandal Malkangiri Medchal Dist, Telangana, India

5School of Business, SR University, Warangal, Telangana, India

6Department of Sociology and Social Work, Himachal Pradesh University, Shimla, India

Abstract

In this evaluation, we identify innovation packages and associated implementation barriers that might be considered to be part of Healthcare 4.0 (H4.0) and investigate their impact on performance enhancement in a sample hospital setting. In order to determine how to proceed, we conducted a cross-sectional review with 181 hospital directors from various nations who have already proactively begun implementing H4.0. Using multivariate statistical methods, the gathered data were examined. According to the findings, H4.0 technologies may be divided into two groups based on how they are used in hospitals. Using the sociotechnical systems theory, common hurdles to H4.0 adoption were empirically divided into two types. H4.0 technology bundles had a good and noticeable impact on how well hospitals performed. It is essential to continuously consider both H4.0 technologies and obstacles since their collaboration with H4.0 barriers adversely affected performance improvement. Our discoveries will help hospital management in expecting likely deterrents to the reception of H4.0, empowering more proactive endeavors to further develop performance and give superior grade, sensibly estimating healthcare in the age of the fourth modern unrest.

Keywords: Healthcare 4.0, hospital, technologies, performance of hospital, barriers

1.1 Introduction

Healthcare 4.0 addresses a significant change in the way healthcare frameworks are viewed. It draws inspiration from Industry 4.0, the fourth industrial revolution [1]. The fourth industrial revolution was propelled by globalization, increased seriousness, advances in information and communications technology (ICT), and market expansion [2]. Because of Industry 4.0, the association went through a computerized change. Steam power was the primary cause of the modern uprising, whereas power molded the second. The third modern upset began in the 1970s as a result of advancements in processing innovation. The fourth industrial revolution was sparked by advances in ICT technologies, which included fully computerized and clever manufacturing processes suitable for independent communication and control among many corporate levels of an association [2–4]. Industry 4.0 features include ongoing information exchange and flexible manufacturing as a result of vertical, horizontal, and end-to-end integration [5]. End-to-end integration is the automated management of a product over its entire life cycle. Vertical integration is the coordination of every authoritative action. Horizontal integration is the combination of all worthy chain parts. In a cycle that consolidates item-focused esteem creation, a progression of exercises is carefully connected together. Examples include analyzing customer needs, developing items, manufacturing them, offering services, maintaining them, and recycling them [3, 6]. According to the definition of Industry 4.0, “Industry 4.0 refers to the integration of Internet of Things (IoT) technologies into industrial value creation, enabling manufacturers to harness fully digitalized, connected, smart, and decentralised value chains, able to deliver greater flexibility and robustness to firm competitiveness and enable them to build flexible and adaptable business structures, [acquiring] the permanent ability for internal evolutionary developments in order to collaborate more effectively with other companies” [2, 7, 8].

Real-time healthcare customization is made possible by H4.0 [9], transforming organizations from hospital-centered to patient-centered ones where many departments collaborate effectively to enhance patient health [10]. Figure 1.1 shows the Changing healthcare systems from healthcare 1.0 to HealthCare 4.0. Hospital staff members can share and use intra- and interhospital services [11, 12] as well as hospital supporting procedures [13, 14] with ease thanks to H4.0. While incorporating new technologies into medical procedures may have a good short-term effect on hospitals’ outputs, the long-term effects of doing so may not be immediately obvious [58].

Figure 1.1 Changing healthcare systems [9].

The technologies that should be considered when implementing H4.0 and how they could interact to increase performance in healthcare frameworks are the subject of current research [15, 16]. Similar to this, agreement has not yet been achieved on how exactly H4.0 technologies affect hospitals’ performance [17]. The digital transformation of healthcare has produced a variety of outcomes [18], especially when one takes into account the role of obstacles brought about by financial and political interests, as well as requests from associations, affiliations, and lobbyists [14].

The phrase “Industry 4.0” was first used in Germany in 2011 as part of a strategic drive to use technology to transform. Our study is based on the sociotechnical systems theory [19], which holds that an organization develops through the right interaction of its social and technical elements, which enhances performance. Sociotechnical systems are a method for organizing complex organizational activity that takes into account how people and technology interact at work [20]. We propose to explore H4.0 taking into account both social and technological components crucial to its implementation, which could help endeavors to be connected to the reception of state of the digital technologies in healthcare organizations [59].

1.2 Literature Review

Since healthcare services are an intangible good, it is difficult to quantify them. It is likewise hard to assess the nature of healthcare services as a result of qualities including immaterialness, heterogeneity, and concurrence [21]. SERVQUAL is one of the most often used metrics for evaluating the quality of healthcare services. Reliability, certainty, tangibles, empathy, and responsiveness are some of its dimensions. This model, in any case, has a disadvantage in that it was created for a general help environment and should be contextualized for the utilization environment [22]. Although there have been numerous studies to gauge the quality of healthcare services [23, 24], Dagger, Sweeney, and Johnson’s model is one of the most widely used. The term “interpersonal quality” refers to the relationships and dyadic communication that have grown between the patient and the consideration supply. It is largely influenced by behavior, communication, and relationships. The expression “relationship” alludes to the degree and nature of the bond that has been laid out between a specialist organization and a customer, whereas the terms “manner” and “communication” describe the information exchanged between a provider and a customer, as well as their level of interaction and two-way communication, respectively [25]. The technical quality includes two aspects. The first and second dimensions are, respectively, the output obtained and the service provider’s technical expertise. Technical competency is determined by the knowledge, experience, credentials, or skill of the provider. The service outcome gives an account of how the service procedure went [60], arising from contacts with a service provider throughout a single or series of service encounters [25, 26].

ICT has been utilized all the more every now and then to help the viability, effectiveness, and quality of healthcare systems [27]. The phrase “Healthcare 4.0” refers to a number of integrated ICT, electronics, and microstructure developments that take into account more effective restorative plans and support functions [28]. The availability of less expensive ICTs that can diagnose problems and offer quick findings and remedies, the shrinking size of ICTs, and improved capacities for data acquisition and management are only a few reasons why ICT usage may be increasing [29]. Healthcare 4.0 is reportedly a transition of healthcare systems [30]. ICT is used in both the hospital’s administrative and medical practices [28]. Lower costs, remote sensor networks for further developed straightforwardness, electronic wellbeing record frameworks, portable wellbeing applications, further developed finding and patient consideration rehearsals [61], support for customized medication possibilities, more limited pausing and lead times, empowering cooperative healthcare, and better help for training and education are only a couple of the advantages of ICT use in healthcare frameworks [25]. The capacity of the healthcare framework to create the ideal outcomes ought to be evaluated in view of its impact on the type of healthcare services. Studies [25, 31–33] have shown the significance of Healthcare 4.0, but no studies have looked at how it will raise the standard of healthcare services. This paper analyzes how Healthcare 4.0 will impact several facets of the standard of medical treatment through a thorough review of the literature [62].

Many studies identify comparable technologies that fall under the H4.0 implementation umbrella. The list includes the Internet of Things, cloud computing, wearable biomedical sensors, and machine learning as enabling influences of an informed perspective on health [34]. Big data analytics is included in the H4.0 portfolios of several studies. Others studied how various technologies were used in healthcare systems, including 3D printing [35], collaborative robots [36], augmented reality [37], and remote monitoring [38]. Despite variations in citation rates, literature research points to the complementary roles that these technologies play.

I4.0 is the continual revolution of conventional industrial and manufacturing procedures using the most cutting-edge smart technology. I4.0 is focused on using large-scale interconnectivity technology deployments to enable better communication, self-monitoring, and higher automation. It also includes smart devices that can evaluate and diagnose problems without requiring human intervention [40]. The term “I4.0” describes a great degree of product personalization under extremely flexible production circumstances [41]. The idea of H4.0 was created as a result of applying I4.0’s technology and design ideas to the healthcare industry [42]. The implementation of digital technology is what spurs the H4.0 approach, which necessitates critical adjustments in healthcare organizations on both the technical and social fronts [43]. With the advent of H4.0, hospitals now have a higher level of automation and connection, which improves both patient care and operational procedures [15].

1.3 Methodology

The research’s methodological approach was empirical because it was exploratory in character. Empirical research, in Goodwin’s opinion, is the most effective way to learn from direct or indirect experience or observation [44]. The previously mentioned research questions can be addressed by measuring empirical information obtained from non-irregular respondents who meet specific prerequisites, which is a typical methodology in investigations of a comparable sort [45]. Due to its many potential benefits, including its elevated degree of representativeness, minimal expense, expected measurable importance, and normalized excitement for all respondents, the study strategy is a typical pick among the information assortment approaches open for empirical examination. Four fundamental components make up the methodology described in this study (see Figure 1.2):

Sample selection and characterization

Creation of data-collecting tools and metrics

Confirmation of the reliability and validity of the constructs

Data analysis. Detailed information on these steps is provided in the following sections.

1.3.1 Selection of the Sample and Characterization

Hospitals from Argentina, Brazil, India, Italy, Mexico, and the USA partook in a worldwide report. We utilized a non-irregular procedure with some foreordained choice measures. To start with, respondents need to have significant positions of authority in their hospitals (center or senior administration), which ought to give them the capacity to picture and comprehend the hospital they address and its unmistakable characteristics [53]. Because of the great degree of intricacy imbued in healthcare organization associations, we likewise searched out a few respondents from various foundations and divisions inside every organization. Such a requirement needs to present a more complete picture of the entire hospital and its H4.0 execution experience [46] given that the evidence on the use of new ICTs may span from regulatory processes to medical treatment. Many respondents per hospital greatly improve the internal validity and reliability of our study and lessen issues caused by single-respondent bias. Notwithstanding the absence of an obviously characterized choice measure for hospitals, we got information from hospitals with different logical qualities. The study’s clinics generally and methodically recorded their exhibition results consistently, taking into consideration more profound information on every respondent’s viewpoint while addressing the inquiry [54].

Figure 1.2 Research process.

As compared with studies that also used surveys as their primary data source, the last example had 181 reactions from 18 hospitals in six nations, with a response rate that is exceptionally high (i.e., 67.8%) [47]. The majority of responders (87.8%) and hospitals (88.9%) were situated in emerging economies. Participants had an average of 57.5% clinical experience in their departments, 69.6% were in supervisor or coordinator roles, and 81.2% had been in their positions for more than 2 years. Also, the majority of responders (61.3% and 59.7%) worked in teaching hospitals, many of which (51.9%) had more than 20 years of history, and 75.7% and 77.9% of responders, respectively, came from institutions with less than 2,000 staff and more than 150 beds.

1.3.2 Creation of a Data-Gathering Tool and Measures

The application form comprised four sections. It first established the demographic profile of the sample by collecting information on the traits of the respondents, their departments, and hospitals. ICT technologies were utilized as a substitute for H4.0 execution since certain respondents probably would not know all about the idea [48]. To check the reception level, we utilized a five-point Likert scale with a scope of 1 (not used) to 5 (completely embraced). The last part of the study requested that respondents rate how their hospitals’ performance has changed over the past 3 years. As per specialists, reactions are bound to be exact when execution changes are utilized as an intermediary for an association’s prosperity, especially when the example is essentially comprised of center directors [49]. Five pointers were used to decide the degree of execution improvement.

1.3.3 Inspection of the Conceptions’ Reliability and Validity

We performed three exploratory factor analyses (EFAs) employing principal component (PC) extraction to assess components using review results. Specialists regularly use EFA to recognize the hidden ideas basic in a bunch of noticed factors [55]. It should be employed in cases like ours where there is no prior knowledge of the components or patterns underlying the observed variables.

Operational performance improvement metrics were used to run the first EFA. Using a varimax turn of the axes, which had an eigenvalue of 3.47 and represented 69.30% of the complete change in the answers, we had the option to achieve critical loadings for the five presentation pointers in the main PC. The Cronbach’s alpha test aftereffect equal to 0.885 exhibited great unwavering quality in reaction to the review’s development, which has an alpha limit of 0.6 or higher [50].

Using feedback on the degree of acceptance of the nine technologies included in the questionnaire, a second EFA was conducted. The objective was to locate H4.0 technology bundles. By rotating with a varimax, we were able to keep two PCs with eigenvalues greater than 1.

The third EFA used responses to the criticality level of eight execution barriers for H4.0. To extract the two components, we use an EFA with varimax rotation and eigenvalues of at least 1. The empirical validation of the two bundles was achieved through barrier variable loading on single components [56]. As suggested by the STS theory, we classified barrier bundles as either social or technological in accordance with their nature, as H4.0 indicates significant changes to how healthcare organizations function. Social hurdles, such as a lack of skilled team members or a strategy that is not in line with the hospital’s strategy, are emotional or intangible issues that may impede the adoption of H4.0.

1.3.4 Data Evaluation

In this step, we initially isolated perceptions into bunches as per the degree of handling activation, correspondence, and detecting innovation reception. To do this, the middle reaction values of each group were utilized as the models to distinguish between low and high adopters. Of the 92 observations, 89 had adoption levels that were higher than the cutoff point and were categorized as “high adopters,” whereas the remaining observations had adoption levels that were lower than the cutoff point [57]. The remaining observations were classified as “high adopters” in the processing-actuation study, whereas 88 respondents were labeled as “low adopters” since their adoption rates were below the bundle’s cutoff.

Comparable advances were taken to characterize perceptions as indicated by H4.0 barriers. In light of the middle reaction upsides of the specialized and social barriers packs, respondents were divided into “lowly constrained” and “highly constrained” groups. Technical challenges were rated as lowly constrained for 80 perceptions and extremely constrained for 101; interpersonal challenges were rated as lowly constrained for 80 perceptions and very constrained for 101.

Using one-way ANOVA, the effect of H4.0 innovation acceptance on the improvement of hospitals’ performance was investigated (dependent variable) [39]. The objective was to assess H1. A two-way ANOVA was used to evaluate the relationships between H4.0 innovations and barriers to hospitals’ exhibition improvement. The Kolmogorov–Smirnov test was used to determine if the residuals from the one- and two-way ANOVA models were ordinarily distributed. The results demonstrated that the errors were independently distributed and on a regular basis.

We ignored the fundamental impact, as Montgomery would have proposed, when both the primary and collaboration impacts were critical. Considering that a base example size is not expected to do ANOVAs [50], the way that we did not test the entire factorial model (counting higher-request communications) because of the little example size is significant.

1.4 Result and Discussion

The results of the ANOVA are displayed in Table 1.1, and they all anticipated that the performance of hospitals will be much enhanced by H4.0 technology. In fact, according to our research, both H4.0 technology bundles and hospital performance are positively associated. In fact, the hospitals’ average performance improvement levels (0.382 and 0.321, respectively) were higher than those of low adopters in terms of detecting correspondence and handling incitation. Given the widespread belief that IT integration enables quicker and more effective operations in healthcare companies, these results are relatively predictable. Our results also support research from studies, which did exploratory tests specifically regions/ treatments inside a clinic [51]. Consequently, when their principal impacts are considered, H4.0 advances in all actuality do appear to work on hospitals’ performance all in all.

Table 1.1 Results of ANOVA.

Independent variable

F

-value of hospital performance

Sensing communication

12.30

Processing actuation

8.64

Sensing communication × technical barriers

3.70

Sensing communication × social barriers

0.06

Processing actuation × technical barriers

0.04

Processing actuation × social barriers

5.32

Two links stand out when analyzing the relationships between H4.0 technologies and constraints: i) the relationship between processing-actuation technologies and social constraints and ii) the relationship between sensing-communication technologies and technical constraints. Using a 90% confidence range, the marginal means of the interactions are estimated. After examining how closely sensing-communication technologies and technical obstacles are related, we come to the conclusion that there is a significant difference between hospitals that adopt sensing-communication technologies at a low rate and those that do so at a high rate, with the latter performing significantly better, when hospital leaders do not view technical barriers as a significant obstacle to the implementation of H4.0 (lowly constrained). These results suggest that a higher level of technical hurdles reduces the beneficial effect of sensing-communication technologies on hospitals’ performance.

This is consistent with the findings of some studies that highlighted the possibility that some technical obstacles, such as IT infrastructure and information security threats, could undermine the projected benefits of the deployment of H4.0 [52]. While healthcare rules and accrediting bodies differ, hospitals that make a major effort to attain compliance are subject to a variety of obligations. Such initiatives are typically time- and money-consuming, and they frequently make it difficult for hospitals to build an effective information and communication system. Our findings corroborate previous findings and show that technical obstacles must be removed in order to fully implement H4.0, particularly when implementing technologies aimed at gathering and disseminating health-related data. All in all, hospitals that need to work on their tasks, administrations, and treatments using sensing-communication technologies may initially have to dispense with the inborn specialized obstacles to understand the advantages of this digital change completely. Figure 1.3 shows the healthcare 4.0 technologies used in hospitals.

Figure 1.3 Healthcare 4.0 technologies for hospitals [63].

1.5 Conclusion

In this article, we looked at how H4.0 technologies, impediments, and their interactions affect the hospitals’ ability to perform better. Two key conclusions are suggested by this research. Initially, two distinct bundles of H4.0 technologies might be created. The most frequent obstacles to the deployment of H4.0 might potentially be divided into two bundles. Second, there is a positive and strong correlation between the performance of hospitals and these H4.0 technology packages. It is crucial to take into account both H4.0 technologies and barriers in order to comprehend how they will affect hospitals because their interaction with H4.0 obstacles was proven to be vital for performance improvement.

The exploration’s discoveries show that executing H4.0 innovation from the two packs improves hospitals’ execution, supporting H1. Unexpected effects on execution are noted while examining the relationship between H4.0 technology packs and technical and social hurdles. Nonetheless, the strength of the relationship impacts may vary depending on the improvements and constraints taken into account.

In order to explain the performance improvement of hospitals, two distinct forms of interactions stood out as important. The first one focuses on technological problems and communication and sensing technologies. We discovered a significant performance improvement gap between low and high users of sensing-communication frameworks that are only marginally constrained by technological constraints. Users with high technology see a positive presentation enhancement of +0.45, while users with low technology see a negative presentation enhancement of −0.38. In sensing-communication technologies that are categorically constrained by specialized limits, the performance improvement gap between low and high users is essential but tangibly less.

The next connection involves overcoming social obstacles and technology for activation. For handling processing-actuation that is severely constrained by social barriers, we discovered a significant performance improvement gap between low and high clients, with low adopters showing a negative improvement of −0.48 and high adopters showing a positive rise of +0.39. In hospitals with a few social restrictions, the performance gap between low and high adopters is statistically insignificant and much less. This surprising finding suggests that the benefits of this technology package become more obvious when hospitals have trouble implementing H4.0 since they miss the mark on vital information, abilities, mentalities, values, and needs. This startling decision to some extent supports the central idea of sociotechnical frameworks hypothesis, which the one-sided or individual support of social or technical parts might deliver accidental communications that adversely affect organizational performance. When taking into account the relationship between societal boundaries and processing-actuation technologies, it seems to be especially obvious.

Future research that makes use of computational simulation tools will also be able to look at how H4.0 implementation could help hospitals function under difficult conditions.

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Notes

*

Corresponding author

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[email protected]

Pramod Kumar: ORCID: 0000-0002-1971-4770Keerthiraj: ORCID:

https://orcid.org/0000-0002-2536-8579

2Human Breast Cancer Classification Employing the Machine Learning Ensemble

Sreenivas Mekala1, S. Srinivasulu Raju2*, M. Gomathi3, Naga Venkateshwara Rao K.4, Kothandaraman D.5 and Saurabh Sharma6

1Department of IT, Sreenidhi Institute of Science and Technology, Telangana, India

2Department of Electronics and Instrumentation Engineering, VR Siddhartha Engineering College, Vijayawada, India

3Department of ECE, S.A. Engineering College, Chennai, India

4Department of ECE, St. Martin’s Engineering College Secunderabad, Telangana, India

5School of Computer Science & Artificial Intelligence, SR University, Warangal, Telangana, India

6Research Scholar, Amity University, Gwalior, India

Abstract

The primary driver of death for women is breast cancer (BC). For the investigation of cancer, the exact order of breast cancer information is fundamental, and patients can save money by avoiding unnecessary surgeries by understanding the difference between benign and malignant tumors. Machine learning is frequently employed in the prediction of breast cancer since it has the benefit of identifying significant traits from a set of medical data. Medical professionals working in the healthcare industry can benefit from and use these decision support tools effectively. The goal of this work is to use ensemble learning to solve the problem of categorizing breast cancer-related data. Techniques for ensemble learning are applied to enhance a classifier’s performance. With the help of a Bayesian network and a radial basis function, an ensemble model for decision support is being developed in this study. This study made substantial use of the well-researched open-access dataset “Wisconsin Breast Cancer Dataset (WBCD).” Oncologists would benefit from the proposed ensemble learning by being able to discriminate cancer tumors precisely and provide patients with the best care.

Keywords: Breast cancer, machine learning, WBCD, classification

2.1 Introduction

Breast cancer is one of the top causes of death among women. In 2011, there were more than 508,000 assessed deaths among women worldwide due to bosom malignant growths, according to a 2013 World Health Organization assessment. Breast cancer in its early stages is treatable and preventable. However, many women receive a cancer diagnosis after it is already too late. Breast cells, oily tissues, or stringy connective tissues can all give rise to breast cancer. Breast cancer tumors typically get worse over time and get bigger, which results in mortality [1]. Men can experience it as well, even though women experience it more frequently. Age and family history are just two of the many factors that can increase the risk of breast cancer. Breast cancer tumors may be risky or innocuous [2]. A benign tumor rarely results in human death and is not harmful to the body. This particular sort of tumor only develops in one area of the body. A cancerous tumor is more harmful and can be fatal to people. The aberrant cell development that causes this form of tumor causes it to spread quickly [38].

2.1.1 Breast Cancer Symptoms and Signs

There are two forms of breast cancer: 1) invasive and 2) non-intrusive. Though invasive cancer spreads to different parts of the body, the second type of cancer simply influences the breast and does not influence different parts of the body. Breast cancer can present with a wide range of symptoms and a few of these are as follows:

Changes in the size and contour of the breast

Dimpling of the breast

A recently turned nipple

A breast that is red

Scaling, peeling, or flaking of the breast’s pigmented region

Breast lump

[3]

2.1.2 Breast Cancer Risk Factors

There are avoidable and non-preventable risk factors for breast cancer.

Mature age

A personal history of breast diseases is a factor that increases the risk of breast cancer.

Personal experience with cancer

Early menarche

Menopause

Late menopause

A history of breast cancer in the family

Obesity

Radiation exposure

Having a first child after turning 30

Null parity is among the other factors

Consuming alcohol

The causes and risk factors of breast cancer are depicted in Figure 2.1.

Figure 2.1 Breast cancer causes and risk factors [4].

2.1.3 Disease Prediction Using Machine Learning

Machine learning (ML), a subset of artificial intelligence, combines a range of factual, probabilistic, and augmenting techniques to enhance performance utilizing both old and new data. To predict diseases, many ML approaches have been widely used.

The early identification of breast cancer was significantly aided by machine learning techniques. According to survey results, machine learning approaches have a 93% accuracy rate, whereas the majority of experienced doctors can identify breast cancer with 76% accuracy. It is simple to differentiate between those who have breast cancer and those who have none by utilizing ML approaches. With proper classification, clinicians will be better equipped to recognize cancer in its early stages [39].

Classification is a difficult regulated improvement issue. Data on cancer are organized using a variety of methods, including SVM, ANN, K-NN, naive Bayes, CNN, and Bayesian network.

The following is a list of significant contributions of our suggested approach.

For the classification of breast cancer data, this research suggests a unique ensemble technique developed employing heterogeneous classifiers like the Bayesian network and radial basis function (RBF).

We enhanced the classifier’s ability to forecast breast cancer.

We carry out our tests using the WBCD dataset from the UCI machine learning library.

2.2 Literature Review

Many researchers have emphasized the use of ML and deep learning in healthcare to provide more excellent and secure consideration [5–13