{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T09:59:23Z","timestamp":1784973563345,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008463","name":"Airlangga Research Fund (ARF) of the Airlangga University, the Republic of Indonesia","doi-asserted-by":"publisher","award":["347\/UN3.LPPM\/PT.01.03\/2024"],"award-info":[{"award-number":["347\/UN3.LPPM\/PT.01.03\/2024"]}],"id":[{"id":"10.13039\/501100008463","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In Indonesia, one of the main causes of death for both young and elderly people is heart attacks, and the main cause of heart attacks is non-communicable diseases such as hypertension. Deaths due to heart attacks caused by non-communicable diseases, namely hypertension, rank first in Indonesia. Therefore, predictions of the risk of having a heart attack caused by hypertension need serious attention. Further, for determining whether a patient is experiencing a heart attack, an effective method of prediction is required. One efficient approach is to use statistical models. This study discusses predicting risk of heart attack via modeling and classifying hypertension risk based on factors that influence it, namely, age, cholesterol levels, and triglyceride levels by using the spline estimator of the Nonparametric Ordinal Logistic Regression (NOLR) model. In this study, we assume an ordinal scale response variable with q categories to have an asymmetric distribution, namely, a multinomial distribution. The data used in this study are secondary data from medical records of cardiac poly patients at the Haji General Hospital in Surabaya, Indonesia. The results show that the proposed model approach has the greatest classification accuracy and sensitivity values compared to NOLR model approach using GAM, and the classical model approach, namely the Parametric Ordinal Logistic Regression (POLR) model. This means that the NOLR model approach is suitable for predicting hypertension and heart attack risks. Also, the NOLR model estimated using the LS-Spline estimator obtained is valid for predicting the risk of heart attack with accuracy value of 85% and sensitivity value of 100%.<\/jats:p>","DOI":"10.3390\/sym16111440","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T06:55:37Z","timestamp":1730357737000},"page":"1440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spline Estimator in Nonparametric Ordinal Logistic Regression Model for Predicting Heart Attack Risk"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1592-4671","authenticated-orcid":false,"given":"Nur","family":"Chamidah","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Budi","family":"Lestari","sequence":"additional","affiliation":[{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Department of Mathematics, Faculty of Mathematics and Natural Sciences, The University of Jember, Jember 68121, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-9487","authenticated-orcid":false,"given":"Hendri","family":"Susilo","sequence":"additional","affiliation":[{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Department of Cardiology and Vascular Medicine, Faculty of Medicine, Airlangga University, Surabaya 60115, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2194-6850","authenticated-orcid":false,"given":"Mochamad Yusuf","family":"Alsagaff","sequence":"additional","affiliation":[{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Department of Cardiology and Vascular Medicine, Faculty of Medicine, Airlangga University, Surabaya 60115, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-4083","authenticated-orcid":false,"given":"I Nyoman","family":"Budiantara","sequence":"additional","affiliation":[{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Department of Statistics, Faculty of Sciences and Data Analytics, Sepuluh Nopember Institute of Technology, Surabaya 60111, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8393-1270","authenticated-orcid":false,"given":"Dursun","family":"Aydin","sequence":"additional","affiliation":[{"name":"Research Group of Statistical Modeling in Life Science, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia"},{"name":"Department of Statistics, Faculty of Science, Mu\u011fla S\u0131tk\u0131 Ko\u00e7man University, Mu\u011fla 48000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1097\/MLR.0b013e31827da594","article-title":"Improved cardiovascular risk prediction using nonparametric regression and electronic health record data","volume":"51","author":"Kennedy","year":"2013","journal-title":"Med. 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