Multi-class heart disease Detection, Classification, and Prediction using Machine Learning Models

TL;DR

This paper introduces new datasets and machine learning models for accurate, real-time heart disease detection tailored to the Bangladeshi population, achieving up to 96.6% accuracy and aiding personalized healthcare.

Contribution

It presents new ethically sourced datasets and applies advanced machine learning models for improved heart disease detection in Bangladesh.

Findings

Random Forest achieved 96.6% accuracy

New datasets include symptoms, examination techniques, and risk factors

AI system provides real-time diagnostics and recommendations

Abstract

Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. However, the development of heart disease detection (HDD) systems tailored to the Bangladeshi population remains underexplored due to the lack of benchmark datasets and reliance on manual or limited-data approaches. This study addresses these challenges by introducing new, ethically sourced HDD dataset, BIG-Dataset and CD dataset which incorporates comprehensive data on symptoms, examination techniques, and risk factors. Using advanced machine learning techniques, including Logistic Regression and Random Forest, we achieved a remarkable testing accuracy of up to 96.6\% with Random Forest. The proposed AI-driven system integrates these models and datasets to provide real-time, accurate diagnostics and personalized healthcare recommendations. By leveraging structured datasets and state-of-the-art machine learning algorithms, this research offers an innovative solution for scalable and effective heart disease detection, with the potential to reduce mortality rates and improve clinical outcomes.