XAI-Diabetes-Prediction 🚀

Predict Diabetes with Transparency: Stacking Ensemble + LIME for Explainable AI

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🌟 Project Overview

This project leverages Explainable Artificial Intelligence (XAI) to predict diabetes using the Pima Indians Diabetes Dataset.

We combine high-accuracy ensemble learning with interpretability:

• Stacking Classifier: KNN, SVM, XGBoost as base learners with Random Forest as the meta-model.
• Data Balancing: SMOTE + ENN for handling imbalanced classes.
• Anomaly Detection: One-Class SVM for removing outliers.
• Explainability: LIME provides interpretable explanations for each prediction.

The workflow ensures that predictions are accurate, robust, and explainable, which is crucial for healthcare applications.

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⚡ Key Features

• Stacking Ensemble: Combines multiple models for better performance
• Data Cleaning: Handles missing values and anomalies effectively
• Balanced Training: SMOTE + ENN ensures fair learning on imbalanced data
• Interpretability: LIME explains individual predictions feature-wise
• Reproducible Workflow: Complete Python notebook ready for experimentation

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📊 Dataset

• Source: [Pima Indians Diabetes Dataset]
• Features:
  • Pregnancies
  • Glucose
  • Blood Pressure
  • Skin Thickness
  • Insulin
  • BMI
  • Diabetes Pedigree Function
  • Age
• Target: Outcome (0 = No Diabetes, 1 = Diabetes)

The dataset contains missing values, outliers, and class imbalance — making it ideal to showcase XAI techniques.

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🛠 Installation

# Clone this repository
git clone https://github.com/fatimasood/XAI-Diabetes-Prediction.git
cd XAI-Diabetes-Prediction

# Install dependencies
pip install -r requirements.txt