A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networks

TL;DR

This paper presents a machine learning and deep neural network-based application for early heart disease diagnosis, achieving 92% accuracy and aiming to reduce misdiagnosis rates and improve patient outcomes.

Contribution

It introduces a novel AI-powered diagnostic application utilizing DNN and ML models with hyperparameter optimization for heart disease detection.

Findings

DNN outperformed Random Forest with 92% accuracy

Hyperparameter tuning improved model performance

Application developed using Flask and Bootstrap

Abstract

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%.