Facilitating heart disease prediction using deep learning models founded on routinely accessible health data

Cardiovascular diseases are the primary cause of non-natural deaths globally, accounting for over 18 million fatalities annually. Therefore, expandable and low-cost cardiac risk prediction systems are crucial for mitigating the impact of heart diseases on human health. In this work, we deploy a Heart Disease Risk Prediction System (HDRPS) ,a deep learning-based cardiac risk prediction system that utilizes affordable health data and electrocardiogram (ECG) images for cardiac risk assessment. In the data prediction segment, a Deep Neural Network Classification Model (DNNCM) was initially developed based on the original 13-feature UCI dataset, achieving a binary classification accuracy of 0.9655. After removing five hard-to-obtain features from the 13-feature UCI dataset, the α part of Heart Disease Risk Prediction Model (HDRPMα), a deep neural network model was developed based on the 8-feature UCI dataset. This model, utilizing eight easily accessible health data points, reached a binary classification accuracy of 0.917. In the image prediction segment, we use a database established from ECG images easily exported from smart wearable devices. The HDRPMβ convolutional neural network model developed for this database achieved an accuracy of 0.95. In the field of AI-driven cardiac disease prediction, HDRPS has significantly improved upon the practical limitations of previous research models, making substantial advances in usability. HDRPS could potentially be employed for national-level large-scale cardiac risk screenings and personal cardiac health monitoring, contributing to humanity's fight against heart disease.