FMLCA: explainable and privacy-preserving federated machine learning classification algorithms for predicting heart...

Heart disease is a global health concern that significantly contributes to worldwide mortality. Machine Learning (ML) models have emerged as a powerful tool for predicting Coronary Artery Disease (CAD), a type of heart disease, by utilizing clinical features for classification. Federated Learning (FL) offers a solution for collaborative training without sharing raw data, thus addressing privacy concerns.

This study presents an innovative approach, Federated Machine Learning Classification Algorithms (FMLCA), which utilizes cloud computing, privacy preservation techniques, and ML classification algorithms, including Decision Tree (DT), Adaptive Boosting (AdaBoost), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to predict CAD. In addition, privacy preserving is considered through the k-anonymity technique, and SHapley Additive exPlanations (SHAP) technique was utilized to identify features important in the model decision-making process.

The proposed RF model, compared to other models, obtained better performance. This RF model achieved an accuracy of 83.21% with privacy preservation and 84.49% without it. Furthermore, the SHAP technique enhances transparency by attributing feature influences in predictions.

Implementing these models on a cloud platform results in efficient computational performance. This proposed approach represents a significant advancement in predictive healthcare tools, capable of accurately predicting CAD across distributed environments. By placing a strong emphasis on privacy and security, this approach underscores its importance and paves the way for a transformative healthcare ecosystem that centers on the needs of patients and healthcare providers.