This project applies Explainable Machine Learning (ML) techniques to predict diabetes using the Pima Indians Diabetes Dataset. With diabetes posing significant health risks globally, the project aims to create interpretable and accurate ML models to assist healthcare practitioners in decision-making. Techniques like SHAP, LIME, and Partial Dependency Plots ensure that the predictions are not only accurate but also comprehensible and actionable.
Dataset Overview:
Source: UCI Machine Learning Repository.
Size: 768 records with 8 features.
Features include Glucose, BMI, Age, Blood Pressure, and others, with "Outcome" indicating diabetes status. Methodology:
Preprocessing: Balanced the dataset using SMOTE to handle class imbalance. Scaled features using Min-Max scaling.
Model Training: Trained an XGBoost classifier for accurate and efficient predictions. Enhanced model performance with Bayesian hyperparameter tuning.
Explainability: Applied SHAP, LIME, and feature importance techniques for model interpretability. Explainability Highlights:
SHAP Summary Plot: Glucose, BMI, and Age were identified as the most critical features for predicting diabetes. LIME Explanations: Provided detailed local explanations for individual predictions. Partial Dependency Plots: Highlighted the relationship between key features and the outcome. Ethical Considerations:
Ensured fairness by examining potential biases in the dataset. Maintained strict compliance with data privacy and security standards (e.g., HIPAA). Key Findings Model Performance:
Achieved high accuracy, precision, recall, and F1-score. Demonstrated strong interpretability with SHAP, enabling better understanding of predictions.
Glucose: The most significant predictor of diabetes.
BMI and Age: Strongly correlated with diabetes risk.
Explainable ML techniques outperform generative AI in terms of interpretability and applicability for healthcare diagnostics. Recommendations
Deploy the model in healthcare settings to assist clinicians in early diagnosis and personalized treatment plans.
Integrate additional features like lifestyle habits or genetic predispositions for improved predictions. Extend the approach to other critical health conditions requiring explainability.
Encourage the use of explainable techniques in sensitive domains like healthcare to build trust and reliability.
Python: For preprocessing, modeling, and evaluation.
Libraries: XGBoost: For classifier training.
SHAP & LIME: For model explainability.
Scikit-learn: For data preprocessing and evaluation metrics.
Pandas/NumPy: For data manipulation.
Jupyter Notebooks: For interactive coding and visualization.
/data/: Contains raw and preprocessed datasets.
/notebooks/: Jupyter notebooks with detailed implementation.
/reports/: Summary reports with visualizations and insights.
/scripts/: Python scripts for preprocessing, training, and evaluation.
Setup Environment: Install required libraries using requirements.txt or a similar setup file.
Run Jupyter Notebooks: Open and execute the provided notebooks to reproduce results.
Explore Explainability Techniques: Use SHAP and LIME outputs to understand model predictions.
Provides interpretable predictions to build trust among healthcare practitioners.
Improves patient outcomes through early detection and targeted interventions.
Sets a standard for using ethical and explainable machine learning in healthcare.
Dataset: Pima Indians Diabetes Dataset from the UCI Machine Learning Repository.
Smith, J.W., Everhart, J.E., et al. (1988). Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus.