Health Risk Level Prediction Using Machine Learning Techniques (Random Forest, XGBoost, and SVM)

Health risk assessment plays a vital role in preventive healthcare by enabling early identification of individuals who may be at risk of developing serious health conditions. Traditional assessment methods often rely on manual evaluation, which can be time-consuming and may not always provide consistent or highly accurate predictions. In this study, we propose an intelligent health risk prediction framework using machine learning techniques to accurately classify an individual's health condition. The proposed system employs three powerful machine learning models: Random Forest, XGBoost, and Support Vector Machine (SVM), to enhance prediction accuracy, robustness, and reliability. The model is designed to classify individuals into three health risk categories: Low, Moderate, and High, based on various health-related attributes obtained from a Kaggle healthcare dataset. By leveraging these advanced machine learning algorithms, the system can effectively identify patterns and relationships within the data, enabling precise risk stratification. This approach supports early intervention, informed clinical decision-making, and improved healthcare management, ultimately contributing to better patient outcomes and overall public health.

Keywords: Health Risk Prediction, Machine Learning, Random Forest, XGBoost, Support Vector Machine (SVM), Healthcare Dataset, Risk Classification, Low Risk, Moderate Risk, High Risk, Predictive Analytics, Preventive Healthcare.