Federated Learning-Based Heart Disease Prediction with Privacy-Preserving Clinical Data Fusion: A Comparative Analysis of Lightweight AI Models for Distributed Healthcare Systems
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Abstract
Background: The fragmentation of health data (silos) and privacy regulations severely limit the development of robust machine learning models for heart disease prediction across distributed healthcare systems. Traditional centralized approaches require data aggregation, which raises serious privacy and regulatory concerns in clinical settings.
Objective: This study aims to develop and evaluate a privacy-preserving federated learning framework for heart disease prediction, enabling collaborative model training across healthcare institutions without compromising patient data privacy.
Method: We implemented a federated learning system using a lightweight neural network architecture across five simulated healthcare institutions (cardiology center, general hospital, emergency unit, and preventive care facility). This framework combines differential privacy mechanisms (ε = 0.718) and uses the UCI Heart Disease dataset with 303 patient records distributed among the participating institutions. Performance is compared with traditional centralized learning approaches using accuracy, precision, recall, F1-score, and AUC metrics.
Results: The federated learning model achieved an accuracy of 77.0% with an AUC of 0.6508 across all participating institutions. The performance of each institution ranged from 72.5% to 82.5% accuracy. The federated approach demonstrated a 2.0% improvement in accuracy compared to centralized learning (75.5%) while fully maintaining data locality. Communication efficiency was achieved through 8 training rounds with only 993 model parameters shared across the network.
Conclusion: Federated learning is a viable solution for privacy-preserving heart disease prediction in distributed healthcare systems, offering performance comparable to or better than centralized approaches while adhering to regulations and data sovereignty.