Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Abstract

Cardiovascular disease (CVD) poses a serious threat to individual health, highlighting the importance of early detection and proactive mitigation. With advancements in consumer electronics such as wearables and IoT, there exists an opportunity for enhanced CVD prediction for users. Machine Learning (ML) has been widely used to predict CVD risk (high/low) based on various factors and is a critical area of healthcare research. However, sharing data needed to predict CVD with machine learning models is challenging due to privacy concerns. Federated Learning (FL) enables distributed training of ML models without sharing raw data. However, it requires all training features to be available to all clients. To address this problem, we propose a Vertical Federated Learning (VFL) based method designed for use with consumer electronics platforms. The proposed method trains Neural Network (NN) model in a distributed manner where different data features are held by different parties. In this work, each party maintains a portion of separate data features, performs calculations on them locally, and then transfers only the necessary information to jointly train an NN model. We employ the proposed method for different use cases where the dataset features are distributed between: i) the patient and the hospital (2-splits); ii) the patient, the doctor, and the laboratory (3-splits); and iii) the patient, the doctor, the Electrocardiogram (ECG) center, and the laboratory (4-splits). Using a realistic dataset publicly available, we test the proposed methodology.