Developing a Logistic Regression Model for Predicting Chronic Kidney Disease

This study presents an optimized Logistic Regression-based predictive model for early detection of Chronic Kidney Disease (CKD) using clinical and demographic data. The framework integrates feature selection techniques and hyperparameter tuning to enhance classification accuracy and interpretability. Unlike traditional approaches, our model is evaluated against Decision Tree, Random Forest, and SVM, ensuring comparative robustness. The results show that the proposed approach achieves an accuracy of X %, outperforming existing models while maintaining low computational complexity. This research contributes to personalized healthcare by offering an early-warning system, improving decision-making for medical practitioners, and enabling timely interventions.