  Heart disease is the leading cause of death worldwide. Currently, 33% of
cases are misdiagnosed, and approximately half of myocardial infarctions occur
in people who are not predicted to be at risk. The use of Artificial
Intelligence could reduce the chance of error, leading to possible earlier
diagnoses, which could be the difference between life and death for some. The
objective of this project was to develop an application for assisted heart
disease diagnosis using Machine Learning (ML) and Deep Neural Network (DNN)
algorithms. The dataset was provided from the Cleveland Clinic Foundation, and
the models were built based on various optimization and hyper parametrization
techniques including a Grid Search algorithm. The application, running on
Flask, and utilizing Bootstrap was developed using the DNN, as it performed
higher than the Random Forest ML model with a total accuracy rate of 92%.
