Early Prediction of Chronic Diseases Using Machine Learning

Healthcare systems are strained by chronic diseases, which cause about 71% of deaths worldwide. Machine learning (ML) models utilizing EHRs, omics, lifestyle, and wearable data can identify pre-disease states months to years before clinical onset, contributing to the shift from reactive to predictive care. The current state of machine learning for early diabetes, heart disease, and hypertension prediction is summarized in this review. Strong discrimination is consistently achieved by deep learning and tree-based ensembles (such as XGBoost); several studies report accuracies/AUCs in the 0.90–0.98 range when rigorously trained and validated. This paper gives a comparative review of few past papers on techniques used, data source, method used of the key findings. We conclude with practical suggestions for models that are generalizable, explainable, and clinically useful. The results highlight the critical role that machine learning plays in enabling early diagnosis, customized treatment, and efficient chronic disease management.