HARNESSING MACHINE LEARNING FOR SYMPTOM-BASED DISEASE PREDICTION

Predicting diseases using symptom data has become increasingly important for delivering faster and moreaccurate medical care. With the rapid growth of digital health records, machine learning (ML) now plays avital role in recognizing hidden patterns from patient data that are often too complex for manual observation.This paper discusses how ML can support symptom-based disease forecasting by building models that learnfrom previous health cases. It also outlines the techniques used to process patient inputs, train models, andevaluate their performance. By integrating ML into clinical workflows, healthcare systems can improvedecision-making, reduce delays, and lower the risk of misdiagnosis.