Heart Guardian: A Model for Predicting Cardiovascular Diseases

Heart disease is a major cause of mortality worldwide and timely detection and intervention being essential to enhancing patient health condition. Recently, deep learning techniques, have shown promising results in various healthcare applications. In this paper, we present a novel deep learning-based model for heart disease prediction that leverages a diverse range of risk factors, medical imaging data, and patient demographics. The proposed model utilizes a Convolutional Neural Network (CNN) for extraction of features from the medical images, which are then fused with the other risk factors and fed into a Multi-Layer Perceptron (MLP) for classification. Deep learning techniques are used to provide insights into the features contributing to the predictions, enhancing the interpretability and transparency of our model. The performance of the model is evaluated using the real-world data set of patients with and without heart disease and compare it to traditional risk assessment approaches. Our study adds to the expanding body of literature on deep learning’s potential in the healthcare industry, highlighting the transformative impact of these models in improving heart disease prediction and enabling personalized interventions. Our findings show that the suggested model performs better than others in terms of accuracy, sensitivity, and specificity, outperforming traditional risk assessment methods.