An artificial intelligence model for heart disease detection using machine learning algorithms

TLDR

The study develops an AI-based heart disease detection system using machine learning algorithms. The aim is to demonstrate that machine learning can predict heart disease risk. The authors built a Python application that preprocesses categorical data, collects databases, applies logistic regression and a random forest classifier to detect heart disease. The random forest model achieved about 83% accuracy, outperforming other methods and demonstrating its potential for accurate heart disease diagnosis.

Abstract

The paper focuses on the construction of an artificial intelligence-based heart disease detection system using machine learning algorithms. We show how machine learning can help predict whether a person will develop heart disease. In this paper, a python-based application is developed for healthcare research as it is more reliable and helps track and establish different types of health monitoring applications. We present data processing that entails working with categorical variables and conversion of categorical columns. We describe the main phases of application developments: collecting databases, performing logistic regression, and evaluating the dataset’s attributes. A random forest classifier algorithm is developed to identify heart diseases with higher accuracy. Data analysis is needed for this application, which is considered significant according to its approximately 83% accuracy rate over training data. We then discuss the random forest classifier algorithm, including the experiments and the results, which provide better accuracies for research diagnoses. We conclude the paper with objectives, limitations and research contributions.