Predictive Analysis System for Early Disease Detection in Healthcare

Predictive analytics has become a critical component in modern healthcare systems, enabling early disease detection and improved clinical decision-making. This study proposes a machine learning-based predictive analytics model for early disease detection using patient clinical data. Multiple supervised learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine were implemented and evaluated. The dataset used for the study consists of patient health indicators such as age, blood pressure, cholesterol level, and glucose level. Data preprocessing techniques such as normalization, missing value handling, and feature selection were applied before model training. Experimental results show that the Random Forest model achieved the highest accuracy compared to other algorithms. The proposed predictive model can assist healthcare professionals in identifying high-risk patients at an early stage, thereby improving treatment outcomes and reducing healthcare costs.