Chronic Kidney Disease Detection with XAI

This project focuses on predicting chronic kidney disease using various machine learning and deep learning models. The dataset contains demographic, credit application specific, credit history, collateral, and financial attributes.

Machine Learning Models

We applied five machine learning models to predict chronic kidney disease:

1. Support Vector Machine (SVM)
2. Logistic Regression
3. Decision Tree
4. XGBoost
5. CatBoost

Results

XGBoost and CatBoost achieved the highest accuracy of 99%.

Deep Learning Models

We also explored Deep Learning models:

1. Artificial Neural Network (ANN)
2. Hybrid Model (LSTM + CNN)

Results

ANN achieved the highest accuracy among deep learning models with 99%.

Explainable AI Techniques

We used Explainable AI (XAI) techniques to interpret the models:

1. SHAP (SHapley Additive exPlanations)

2. LIME (Local Interpretable Model-agnostic Explanations)