Applied Machine Learning: Concepts, Algorithms, and Real-World Applications
Articles

Heart Disease Prediction using Supervised Machine Learning Algorithms: Performance Analysis and Comparison

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  • Jayanta Chowdhury
Jayanta Chowdhury
Swami Vivekananda University, Barrackpore, India

Published 2026-07-06

Keywords

  • Heart Disease Prediction,
  • Supervised Machine Learning,
  • Algorithm Comparison,
  • Logistic Regression,
  • Support Vector Machine,
  • Decision Tree,
  • Random Forest,
  • Healthcare,
  • Classification Algorithms,
  • Performance Evaluation
    • ...More
    Less

How to Cite

Chowdhury, Jayanta. “Heart Disease Prediction Using Supervised Machine Learning Algorithms: Performance Analysis and Comparison”. Swami Vivekananda University Press, July 2026, pp. 63-66, https://swamivivekanandauniversitypress.com/index.php/svup/article/view/655.

Copyright (c) 2026 Swami Vivekananda University Press

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Heart disease is one of the leading causes of death worldwide, making early prediction crucial for effective intervention. This paper explores the use of supervised machine learning algorithms for predicting heart disease risk based on various patient data. The study compares multiple algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Random Forest, evaluating their performance in terms of accuracy, precision, recall, and F1-score. The data used for this research includes patient information such as age, sex, cholesterol levels, blood pressure, and other clinical parameters. By analyzing the results, the study aims to identify the most effective algorithm for heart disease prediction and provide insights into the strengths and weaknesses of each approach. The findings highlight the potential of machine learning in transforming healthcare by enabling faster and more accurate predictions, contributing to better healthcare outcomes.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.63-66

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