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Theoretical Perspectives on Machine Learning in the Diagnosis of Coronary Heart Disease: A Comparative Framework for Clinical Decision Support

Authors

  • MD Rahat Hossain

    School of Mechanical engineering , China
    Author
Keywords:
Coronary Heart Disease, Machine Learning, Classification.
Abstract
Coronary Heart Disease (CHD) remains one of the most prevalent causes of global mortality, posing a continuous challenge to modern healthcare systems. Traditional diagnostic methods, while effective, are often limited by subjectivity, time constraints, and the increasing complexity of patient data. Machine learning (ML) offers a theoretical framework that can revolutionize CHD diagnosis by enabling automated, data-driven decision-making. This paper explores the theoretical basis of using ML algorithms—specifically Logistic Regression, Random Forest, and Support Vector Machine (SVM)—for CHD diagnosis. We present a conceptual comparison of these models in terms of learning mechanisms, data interpretability, clinical applicability, and model complexity. Rather than focusing on numerical results, this paper provides a high-level analysis of how these models theoretically contribute to improving diagnostic accuracy, patient stratification, and clinical workflow efficiency.
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Published
05/16/2025
Issue
Vol. 1 No. 2 (2025): The Science Post
Section
Articles
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Copyright (c) 2025 MD Rahat Hossain (Author)

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

How to Cite

Theoretical Perspectives on Machine Learning in the Diagnosis of Coronary Heart Disease: A Comparative Framework for Clinical Decision Support. (2025). The Science Post, 1(2). https://www.thesciencepostjournal.com/index.php/tsp/article/view/38