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Santhi Priya G. is an Assistant Professor in the Department of Instrumentation at NSS College, Nemmara, Palakkad, Kerala. Her contribution focuses on machine learning applications in healthcare diagnosis and disease prediction.
Machine Learning (ML) has surfaced as a formidable tool for enhancing healthcare diagnostics, disease forecasting, patient surveillance, and clinical decision processes. The growing accessibility of electronic health records, medical imaging, laboratory results, wearable sensor data, and genomic data has facilitated the creation of intelligent systems that can discern intricate patterns to assist healthcare practitioners. This section elucidates the essential principles and significant uses of machine learning in medical diagnostics and illness forecasting. It examines sources of healthcare data, data preprocessing techniques, feature selection methods, supervised and unsupervised learning paradigms, and frequently utilised algorithms including Decision Trees, Random Forest, Support Vector Machines, k-Nearest Neighbours, Naive Bayes, XGBoost, Artificial Neural Networks, Convolutional Neural Networks, and recurrent deep learning architectures. Prominent domains of use encompass cardiovascular illness forecasting, diabetes identification, cancer identification, neurological disease forecasting, respiratory disease evaluation, kidney disease forecasting, and medical imaging analysis. The section additionally explores predictive analytics, tailored medicine, remote patient surveillance, interpretable AI, federated learning, and the amalgamation of machine learning with IoT and cloud healthcare infrastructures.