From Data to Treatment: Early Detection of Heart Disease Using Machine Learning Techniques

Yousofi Tezerjan , Mostafa; Mollabagher, Maryam

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Volume 34, Issue 1 (3-2026)

Journal of Ilam University of Medical Sciences 2026, 34(1): 72-89

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From Data to Treatment: Early Detection of Heart Disease Using Machine Learning Techniques

Mostafa Yousofi Tezerjan¹

, Maryam Mollabagher ^*²

1- Dept of Industry, University of Applied Science and Technology, Tehran, Iran
2- Dept of Management and Social Services, University of Applied Science and Technology, Tehran, Iran , mollabagher@uast.ac.ir

Abstract: (448 Views)

Introduction: Early detection of cardiovascular diseases (CVDs) is a significant challenge in the medical field. As one of the leading causes of mortality worldwide, CVDs require timely and accurate diagnosis for effective prevention. Delayed diagnoses often result from limitations in traditional methods and the insufficient identification of risk factors. This study aimed to enhance the accuracy of heart disease prediction using machine learning techniques.
Materials & Methods: This research assessed the detection rate and accuracy of cardiovascular disease prediction using machine learning algorithms, including logistic regression, the C5.0 decision tree, neural networks, and mixed models. Data from the Cleveland Clinic dataset, comprising 303 samples and 13 clinical features, were analyzed. Performance metrics such as accuracy, sensitivity, and specificity were used to evaluate the models. Data analysis was conducted using SPSS Modeler 18 software.
Results: The evaluated models demonstrated varying accuracies ranging from 85% to 93%. The hybrid model achieved the best performance with an accuracy of 93.44%. Additionally, ST slope, chest pain type, and fasting blood sugar were identified as the most significant risk factors for cardiovascular diseases.
Conclusion: Machine learning algorithms offer significant potential for the early detection of cardiovascular diseases, thereby reducing mortality rates. These techniques enable precise analysis of clinical data, improve medical decision-making, and enhance treatment outcomes.

Keywords: Cardiovascular Diseases, Early Diagnosis, Machine Learning, Risk Factors, Predictive Analytics

Full-Text [PDF 775 kb] (193 Downloads)

Editorial Note: Research | Subject: stats
Received: 2025/11/26 | Accepted: 2026/02/25 | Published: 2026/03/25

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Yousofi Tezerjan M, Mollabagher M. From Data to Treatment: Early Detection of Heart Disease Using Machine Learning Techniques. J. Ilam Uni. Med. Sci. 2026; 34 (1) :72-89
URL: http://sjimu.medilam.ac.ir/article-1-8846-en.html

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