FedCVD: Towards a Scalable, Privacy-Preserving Federated Learning Model for Cardiovascular Diseases Prediction

Abstract

This paper presents FedCVD, a federated learning model designed for predicting cardiovascular disease (CVD) by employing logistic regression and Support Vector Machine (SVM) algorithms. FedCVD utilizes the privacy and scalability advantages offered by federated learning to facilitate collaborative model training using decentralized patient data, ensuring confidentiality. To evaluate the effectiveness of the proposed model, experiments were conducted using the 10-year risk of coronary heart disease Kaggle dataset. To address data imbalance challenges, three techniques—Random Over Sampling, Random Under Sampling, and Synthetic Minority Oversampling Technique (SMOTE)—were explored. The study demonstrates promising performance,For the federated logistic regression with SMOTE achieving an AUC value of 0.7048. Comparative analysis with a centralized logistic regression model shows competitive results, with an AUC value of 0.7081 using Random Over Sampling. For the federated SVM model, an AUC value of 0.7340 is achieved using Random Under Sampling. In comparison, a centralized machine learning approach utilizing SVM and Random Over Sampling achieves an AUC value of 0.6962. These findings highlight the effectiveness of the proposed federated learning approach, surpassing the performance of centralized machine learning models for CVD prediction.

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