Spline Estimator in Nonparametric Ordinal Logistic Regression Model for Predicting Heart Attack Risk

Abstract

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%.