Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression
Coronary Heart Disease (CHD) represents a significant public health challenge in Indonesia, contributing substantially to morbidity and mortality rates. We propose a new method for modeling CHD risk by considering the influencing factors, namely age, fatty food consumption and anxiety using Penalize...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125001669 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849471531720114176 |
|---|---|
| author | Nur Chamidah Budi Lestari Hendri Susilo Triana Kesuma Dewi Toha Saifudin Naufal Ramadhan Al Akhwal Siregar Dursun Aydin |
| author_facet | Nur Chamidah Budi Lestari Hendri Susilo Triana Kesuma Dewi Toha Saifudin Naufal Ramadhan Al Akhwal Siregar Dursun Aydin |
| author_sort | Nur Chamidah |
| collection | DOAJ |
| description | Coronary Heart Disease (CHD) represents a significant public health challenge in Indonesia, contributing substantially to morbidity and mortality rates. We propose a new method for modeling CHD risk by considering the influencing factors, namely age, fatty food consumption and anxiety using Penalized Spline Nonparametric Logistic Regression (PSNLR) approach to analyze the CHD and non-CHD risks affected by these influencing factors. The proposed method gave results a percentage of model classification accuracy of 94.31 % and the value of area under the receiver operating characteristic curve (AUC) of 0.96. This means that the proposed method is a powerful method in modeling the CHD risk and it can effectively identify the nonlinear relationship between influencing factors and CHD incidence. This research supports the achievement of Sustainable Development Goals (SDGs) point 3, especially the target of reducing deaths from non-communicable diseases such as CHD through improving disease prevention and treatment. The use of more accurate models in identifying risk factors also contributes to efforts to improve overall public health and strengthen health care systems. • A cross-sectional survey on influencing factors of CHD risk was conducted at the Airlangga University Hospital. • For analyzing relationship between influencing factors and CHD risk, we used PSNLR method. |
| format | Article |
| id | doaj-art-d516e86f0c014c27831c34039fca7b7e |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-d516e86f0c014c27831c34039fca7b7e2025-08-20T03:24:48ZengElsevierMethodsX2215-01612025-06-011410332010.1016/j.mex.2025.103320Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regressionNur Chamidah0Budi Lestari1Hendri Susilo2Triana Kesuma Dewi3Toha Saifudin4Naufal Ramadhan Al Akhwal Siregar5Dursun Aydin6Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Corresponding author.Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Department of Mathematics, Faculty of Mathematics and Natural Sciences, The University of Jember, Jember 68121, IndonesiaResearch Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Department of Cardiology and Vascular Medicine, Faculty of Medicine, Airlangga University, Surabaya 60286, IndonesiaDepartment of Psychology, Faculty of Psychology, Airlangga University, Surabaya 60286, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, IndonesiaResearch Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Mathematics Master Study Program, Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, IndonesiaResearch Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia; Department of Statistics, Faculty of Science, Muğla Sıtkı Koçman University, Muğla 48000, Turkey; Research Scholar at Department of Mathematics, University of Wisconsin, Oshkosh Algoma Blvd, Oshkosh, WI 54901, USACoronary Heart Disease (CHD) represents a significant public health challenge in Indonesia, contributing substantially to morbidity and mortality rates. We propose a new method for modeling CHD risk by considering the influencing factors, namely age, fatty food consumption and anxiety using Penalized Spline Nonparametric Logistic Regression (PSNLR) approach to analyze the CHD and non-CHD risks affected by these influencing factors. The proposed method gave results a percentage of model classification accuracy of 94.31 % and the value of area under the receiver operating characteristic curve (AUC) of 0.96. This means that the proposed method is a powerful method in modeling the CHD risk and it can effectively identify the nonlinear relationship between influencing factors and CHD incidence. This research supports the achievement of Sustainable Development Goals (SDGs) point 3, especially the target of reducing deaths from non-communicable diseases such as CHD through improving disease prevention and treatment. The use of more accurate models in identifying risk factors also contributes to efforts to improve overall public health and strengthen health care systems. • A cross-sectional survey on influencing factors of CHD risk was conducted at the Airlangga University Hospital. • For analyzing relationship between influencing factors and CHD risk, we used PSNLR method.http://www.sciencedirect.com/science/article/pii/S2215016125001669Penalized Spline Nonparametric Logistic Regression (PSNLR) |
| spellingShingle | Nur Chamidah Budi Lestari Hendri Susilo Triana Kesuma Dewi Toha Saifudin Naufal Ramadhan Al Akhwal Siregar Dursun Aydin Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression MethodsX Penalized Spline Nonparametric Logistic Regression (PSNLR) |
| title | Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| title_full | Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| title_fullStr | Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| title_full_unstemmed | Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| title_short | Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| title_sort | modeling coronary heart disease risk based on age fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression |
| topic | Penalized Spline Nonparametric Logistic Regression (PSNLR) |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125001669 |
| work_keys_str_mv | AT nurchamidah modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT budilestari modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT hendrisusilo modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT trianakesumadewi modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT tohasaifudin modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT naufalramadhanalakhwalsiregar modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression AT dursunaydin modelingcoronaryheartdiseaseriskbasedonagefattyfoodconsumptionandanxietyfactorsusingpenalizedsplinenonparametriclogisticregression |