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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Heart disease prediction using logistic regression 

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pp. 1–16Online FirstMay 2026DOI: 10.47974/JSMS-1587XML
Received:
02 Jun 2025
Published Online:
08 May 2026
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1587
Pages:
1–16

Abstract

Heart disease remains a major global health concern and one of the leading causes of mortality, emphasizing the importance of early detection and reliable risk assessment. This research investigates the effectiveness of Logistic Regression (LR) in predicting heart disease risk using the Heart Disease Statlog dataset. Various clinical and demographic parameters including age, cholesterol levels, exercise-induced angina, and ST depression were examined to build a predictive model. The dataset was subjected to preprocessing, feature selection, and exploratory data analysis (EDA) before model training. Evaluation metrics such as accuracy (92.59%), precision (94.7%), recall (85.7%), and AUC-ROC (0.95) demonstrated strong model performance. The study underscores the interpretability of LR for medical diagnosis while recognizing its limitations in modeling complex non-linear patterns. Future studies may enhance prediction accuracy by employing advanced machine learning techniques and integrating real-time health monitoring systems. Overall, the findings support improvements in early disease detection, healthcare decision-making, and actuarial risk analysis in the health insurance sector.

Keywords

Heart diseasePredictive modelLogistic regressionRisk assessmentMachine learning

Subject Classifications

15A0660G5762H1262H35

References

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