Prediction of Heart Attack Risk and Detection of Sleep Disorders Using Deep Learning Approach

Abstract

This study presents a novel deep learning-based approach for predicting heart attack risk and detecting sleep disorders. Traditional models often fall short in accessibility and accuracy, leading to substandard outcomes. Our proposed ensemble-based AI model overcomes these limitations by leveraging the strengths of deep learning techniques, including neural networks, for robust analysis of sleep patterns and heart health indicators. Our model is capable of integrating data from diverse sources including wearable devices and health records. Emphasis is placed on interpretability, enabling users to comprehend predictions and take informed actions to improve their well-being. Through extensive evaluation and validation, our model demonstrates superior performance in accurately predicting heart attack risk and identifying various sleep disorders. In This research paper we have focused on advancing preventive healthcare strategies by enabling early detection and intervention, ultimately enhancing patient outcomes and healthcare resource utilization.