Heart Disease Prediction and Diagnosis Using Deep Learning Techniques

Abstract

Heart disease is still a major global health concern, but its effects can be significantly diminished via early detection and prevention. Using a combination of established risk variables and cutting-edge machine learning methods and For the predicted accuracy, a machine learning ensemble model is used. This ensemble model combines neural networks, support vector machines, and decision trees to accurately represent intricate nonlinear interactions between the variables. Validation of sizable and varied patient dataset, including both those with and without cardiac disease, is used to run the model. The model's performance indicators, such as accuracy, precision, recall, and F1-score, are used to assess how well it can identify those who are at high risk. To demonstrate the improvements in accuracy and dependability, comparisons are done against current risk prediction models. The findings show the integrated model's improved predictive power and offer clinicians and other healthcare professionals a useful tool for identifying those who are more likely to acquire heart disease. By providing insights into the main causes of the disease, this method may help develop customized preventive measures. The suggested integrated predictive model has promise for enhancing public health outcomes and fostering proactive cardiac treatment because heart disease continues to be a major cause of mortality.