Heart Disease Federated Prediction in Python Projects

Heart Disease Federated Prediction in Python Projects

Abstract

Heart disease remains one of the leading causes of mortality worldwide, and early prediction is critical for preventive healthcare. The project Heart Disease Federated Prediction in Python Projects focuses on developing a privacy-preserving predictive system that leverages federated learning to analyze distributed patient data without sharing sensitive medical records. Python is used as the development platform due to its extensive libraries for machine learning, deep learning, and data handling, including TensorFlow Federated, PySyft, Pandas, NumPy, and Keras. The system allows multiple hospitals or medical institutions to collaboratively train machine learning models on their local data while maintaining data privacy. By integrating federated learning with predictive models for heart disease, the system enables accurate, secure, and scalable prediction of cardiovascular risks, assisting clinicians in early intervention and personalized treatment planning.

Existing System

Existing heart disease prediction systems typically rely on centralized data collection, where patient records from multiple sources are aggregated in a single database for analysis. While effective in modeling patterns, centralized approaches raise concerns over data privacy, security, and compliance with regulations such as HIPAA or GDPR. Some systems employ traditional machine learning models like Logistic Regression, Random Forest, or SVM on local hospital datasets, but these models are limited in generalizability due to small sample sizes and lack of collaborative learning. Additionally, central aggregation of sensitive health data increases the risk of breaches and unauthorized access, which makes conventional approaches less suitable for large-scale, privacy-conscious healthcare environments.

Proposed System

The proposed system introduces a Python-based federated learning framework for heart disease prediction. Patient data remains localized within each participating institution, and only model updates (gradients or parameters) are shared with a central server for aggregation. Data preprocessing, feature extraction, and normalization are performed locally on each site to maintain consistency. Machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and deep learning models such as Artificial Neural Networks (ANN) or CNNs, are trained in a federated setup. The aggregated global model benefits from knowledge across multiple institutions without exposing sensitive patient information. Performance evaluation is conducted using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. By combining federated learning with predictive analytics, the system ensures secure, collaborative, and scalable heart disease prediction, enabling early diagnosis, risk assessment, and improved healthcare outcomes.