Machine Learning-Based Predictive Models for Early Disease Detection

Abstract

Early detection of disease allows treatment before irreversible damage occurs and can improve survival and quality of life. Clinical prediction models, which estimate an individual's risk of having or developing a disease, have long supported early detection; examples include the Framingham risk score and the Indian Diabetes Risk Score. Machine learning (ML) extends these approaches by learning complex, non-linear relationships from large and diverse data sources, including electronic health records, laboratory results, images, physiological signals, genomics and wearable devices. This review describes how ML-based predictive models are developed and evaluated, and summarises their applications in early disease detection. The development pipeline includes defining the clinical question and outcome, assembling and preparing data, selecting and training algorithms (such as regularised regression, random forests, gradient boosting and neural networks), and validating performance internally and externally. Rigorous evaluation requires assessment not only of discrimination, typically by the area under the receiver operating characteristic curve, but also of calibration and clinical utility, for example by decision curve analysis. Applications include early warning of sepsis and clinical deterioration, prediction of acute kidney injury, identification of undiagnosed diabetes and cardiovascular risk, detection of cancer and of diabetic retinopathy, prediction of chronic kidney disease progression and dementia, and maternal and neonatal risk prediction. Despite promising results, systematic reviews show that many published models are at high risk of bias, are rarely externally validated and seldom improve on simpler models. Further challenges include dataset shift, algorithmic bias, limited interpretability, alert fatigue and integration into workflows. Adherence to reporting and appraisal standards such as TRIPOD+AI and PROBAST, external and prospective validation, impact studies and continuous monitoring are essential before ML models can improve early disease detection in practice.