Early Risk Prediction System Using Machine Learning for Preventive Healthcare

Contemporary healthcare systems increasingly prioritize early disease detection and prevention strategies, driven by the growing burden of chronic illnesses and escalating medical expenses. This research introduces a predictive framework utilizing machine learning techniques to assess individual risk profiles through comprehensive lifestyle and clinical indicators. The analytical dataset incorporates variables including age demographics, body mass index measurements, tobacco use patterns, alcohol intake frequency, and exercise participation rates. The implementation leverages a Random Forest classification algorithm, selected for its computational stability and capacity to process multifaceted data patterns effectively. In contrast to conventional diagnostic approaches that focus on existing conditions, this methodology emphasizes preemptive risk assessment to facilitate early intervention strategies. Performance analysis reveals the model delivers superior accuracy rates, maintains optimal precision-recall equilibrium, and provides consistent predictive outcomes. The framework supports seamless integration within dynamic healthcare platforms, promoting preventive medical practices while minimizing future healthcare expenditures