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All Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Marisa Rifada
Universitas Airlangga

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Modelling Of Coronary Heart Disease Risk Using Penalized Spline Semiparametric Logistic Regression Based On Hypertension History And Fatty Food Consumption Naufal Ramadhan Al Akhwal Siregar; Nur Chamidah; Marisa Rifada
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ja9m9g46

Abstract

Coronary heart disease (CHD) remains a critical global health challenge, necessitating precise statistical modeling to unravel its complex risk factors. This study applies a binary semiparametric logistic regression model with a penalized spline estimator (SLR-PS) to investigate these determinants effectively. The model achieved a classification accuracy of 70.45% and a promising sensitivity of 77.8%. In clinical settings, this sensitivity is paramount as it ensures the accurate identification of true positive cases, minimizing the risk of undiagnosed severe conditions. The findings unveil a significant nonlinear effect of fatty food intake on CHD risk, emphasizing the critical role of dietary control. Parametrically, individuals with a history of hypertension are found to be 4.641 times more likely to experience CHD compared to their counterparts, while each incremental unit of fatty food intake is associated with a 1.8% increase in CHD odds. These results highlight the urgency of managing hypertension and reducing dietary fat to mitigate cardiovascular risks, directly contributing to the advancement of Sustainable Development Goal (SDG) 3: Good Health and Well-Being. Future research is recommended to expand this framework by incorporating physical activity, genetic predisposition, and stress to further enhance predictive accuracy and support evidence-based preventive strategies.
Co-Authors Naufal Ramadhan Al Akhwal Siregar Nur Chamidah