

Heart diseases stand as a leading cause of mortality worldwide, posing a particularly severe challenge in developing regions across Africa and Asia. Detecting heart diseases in their early stages not only empowers patients to take preventive measures but also equips healthcare practitioners to discern and mitigate the primary causes before an actual heart attack occurs. This paper introduces CardioHelp, a method devised to predict the likelihood of cardiovascular disease in patients. This method integrates a deep learning algorithm known as convolutional neural networks (CNN), specifically focused on temporal data modeling for early-stage heart failure prediction.