Studying Into the Diagnostic and Therapeutic Applications of Machine Learning Algorithms in Medicine: Medicinal Applications Using Machine Learning

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.