Improving Heart Disease Prediction: Insights from Federated Deep Learning

Heart disease prediction has become an important domain in the current scenario, with numerous research efforts aimed at developing systems capable of detecting heart disease before its severe consequences occur. Machine Learning technology plays a crucial role in developing predictive models for disease prediction, but it also raises concerns about data privacy and security. To address such privacy issues, the proposed work introduces the Federated Deep Learning model training, where Deep Learning models are trained using a privacy-preserving approach known as Federated Learning. The proposed work explores the effectiveness of hybrid deep learning models combining long short-term memory networks and convolutional neural networks for classifying electrocardiogram signals and predicting heart diseases. The work utilizes a comprehensive dataset of ECG recordings and employs rigorous training and evaluation processes to assess the model's performance. The result demonstrates the model's proficiency in accurately classifying diverse ECG signal patterns and their potential for robust heart disease prediction. This research contributes to advancing cardiovascular health diagnostics by proposing a federated deep learning approach that combines spatial and temporal information to enhance the accuracy of heart disease prediction.