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Studying Into the Diagnostic and Therapeutic Applications of Machine Learning Algorithms in Medicine: Medicinal Applications Using Machine Learning
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Author(s): Swati Shirke (Pimpri Chinchwad University, India), Rahul Ganpatrao Sonkamble (Pimpri Chinchwad University, India), Sonali Patil (G.H. Raisoni College of Engineering and Management, Pune, India), Jayashree Rajesh Prasad (MIT Art, Design, and Technology University, India), Sudeep D. Thepade (Pimpri Chinchwad College of Engineering, India)and Divya Midhunchakkaravarthy (Lincoln University College, Selangor, Malaysia)
Copyright: 2025
Pages: 36
Source title:
Assistive Technology Solutions for Aging Adults and Individuals With Disabilities
Source Author(s)/Editor(s): Rajiv Pandey (Amity University, Lucknow, India), Pratibha Maurya (Amity University, Lucknow, India), Jigna Bhupendra Prajapati (Ganpat University, India), Raju Halder (Indian Institute of Technology, Patna, India)and Kanishka Tyagi (UHV Technologies, USA)
DOI: 10.4018/979-8-3693-6308-9.ch008
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Abstract
Machine learning (ML) is increasingly transforming healthcare, particularly in disease diagnosis, by improving precision, efficiency, and personalization. The chapter investigates ML's role in early disease detection, addressing the shortcomings of traditional diagnostic methods, including high costs and the need for expert interpretation. Through a bibliometric analysis of 1,216 journals from Web of Science (WOS) and Scopus, the study identifies key contributors and influential works in ML-based disease diagnosis. It evaluates the accuracy and challenges of different ML techniques, including decision trees, support vector machines, and convolutional neural networks. Ethical, regulatory, and data privacy issues critical for integrating ML into clinical practice are also discussed. Furthermore, the research introduces a novel ML architecture designed for disease diagnosis, integrating data from diverse sources and emphasizing continuous learning, privacy, and security.
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