A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networks
This work addresses heart disease diagnosis for medical professionals, but it is incremental as it applies existing methods to a known dataset.
The paper tackled the problem of heart disease misdiagnosis by developing an application using machine learning and deep neural networks, achieving a 92% accuracy rate with a DNN model on the Cleveland Clinic dataset.
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%.