GR-406 Federated Learning in Cardiac Diagnostics: Balancing Predictive Accuracy with Data Privacy in Heart Sound Classification

Cardiovascular diseases account for nearly a third of global deaths, posing a challenge that machine learning can help address. However, data privacy concerns hinder the direct application of conventional machine learning in this sensitive area. This paper explores Federated Learning (FL) as a decentralized strategy to mitigate these concerns by allowing for local data processing. FL\u27s design ensures that only processed updates, not raw data, are shared with a central server, maintaining individual privacy. Our research assesses FL\u27s practicality and effectiveness in predicting heart disease while adhering to ethical and legal norms. We build upon previous studies, such as Wanyong et al.\u27s work on heart sound analysis with FL, to underline its privacy-preserving benefits. This study aims to improve healthcare outcomes with machine learning while setting a privacy-conscious benchmark for future research

GR-325 Early Heart Disease Detection Using Mel-Spectrograms and Deep Learning

Heart diseases are the leading cause of mortality worldwide, emphasizing the need for early detection and intervention. Traditional heart sound analysis using a stethoscope is subjective and prone to variability, necessitating a more objective and reliable approach. In this study, we present a deep learning model designed for heart sound analysis to enable the early detection of heart diseases. The model\u27s architecture combines convolutional and fully connected layers with max-pooling and dropout operations, effectively capturing intricate patterns in heart sounds. We trained and validated our model on the Physionet 2016 challenge dataset, consisting of 3240 labeled heart sound recordings. Our deep learning model achieved an accuracy of 91.9%, surpassing the current state-of-the-art performance of 89.7%. This result demonstrates the model\u27s potential to significantly reduce diagnostic errors and facilitate timely interventions, ultimately improving patient outcomes and reducing healthcare costs

Federated Learning in Cardiac Diagnostics: Balancing Predictive Accuracy with Data Privacy in Heart Sound Classification

Cardiovascular diseases represent a significant global health concern, accounting for 31% of all worldwide deaths. While machine learning presents a promising avenue for early and accurate diagnosis, the associated ethical and legal challenges, especially concerning data privacy, complicate its direct application. This research paper delves into Federated Learning (FL), a decentralized method, as a potential solution to address data utility and privacy concerns. FL enables devices or servers to hold subsets of overall data, compute local updates, and relay them to a central server without transferring raw data, thus maintaining privacy. The study aims to evaluate the feasibility and efficacy of applying FL to heart disease prediction while maintaining ethical and legal standards. Prior work in this domain, particularly by Wanyong et al., utilized FL for heart sound analysis, highlighting its advantages in data privacy and decentralization. Drawing on this background, our research contributes to the dual objectives of enhancing healthcare outcomes and ensuring data privacy, setting a benchmark for the future application of machine learning in medical research