Authors: Pratik Shankarrao Shirke, Professor A. P. Bangar, Professor S. B. Bhosale, Professor V. A. Karad
Abstract: Chronic and life-threatening illnesses are frequently found late, and assessing risk by hand is slow and uneven across large groups of patients. This paper offers a focused narrative review that brings together five studies on machine-learning-based disease prediction published by IEEE and Elsevier: a convolutional-network risk model built on regional hospital data; a hybrid random-forest and linear model for heart disease; a preprocessing-and-ensemble framework for diabetes; a smart monitoring system that combines sensor data and medical records through ensemble deep learning; and a study of breast tumour classification. The review follows three connected themes: the data sources and prediction models used, how strong and how comparable the reported evidence is, and the conditions that matter when prediction is carried into a clinical decision-support pipeline. Only one study could be read in full. It reports an area under the ROC curve (AUC) of 0.950, with sensitivity 0.789 and specificity 0.934, for a weighted ensemble on the PIMA Indians Diabetes dataset. The other four were assessed from their published abstracts and report, among other results, accuracies of 94.8%, 88.7% and 98.5%; these figures involve different diseases, data and metrics, so they cannot be ranked against one another. The review contributes a comparative table, a conceptual architecture for the proposed disease prediction system (not experimentally validated), and a prioritised agenda of research gaps.
