A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease

This project applies Explainable AI (XAI) to ensemble models for predicting Chronic Kidney Disease (CKD) using a Kaggle dataset. Machine learning algorithms such as Logistic Regression, Random Forest, SVM, KNN, Naive Bayes, and FNN are employed for CKD risk prediction. LIME (Local Interpretable Model-Agnostic Explanations) ensures transparency in the results. The system is deployed as a web application with HTML, CSS, JavaScript, and Flask, allowing users to input clinical data and receive predictions with interpretable explanations.