Modeling of hypertension risk factors using local linear of additive nonparametric logistic regression

Abstract

Abstract Hypertension has become a serious health problem in Indonesia because of its prevalence, however, the causative factors could not be ascertained for about ninety percent of the patients. Various studies have found several risk factors causing hypertension to be obesity, family history, stress levels, heart rate, and an unhealthy lifestyle. In this case, the variables are considered influential on hypertension through a regression curve without a specific pattern. Also, we need to describe the functional relationships between several predictor variables with binary or dichotomous response variables and need to describe locally effect of predictor variables to the response variable. Therefore, in this study, to model the case of hypertension by age, body mass index, heart rate, stress levels we use the additive nonparametric logistic regression approach based on local linear estimators. The results of the study showed that hypertension was most prevalent among respondents over 65 years of age with BMI between 25-30 kg/m2 (obesity) and normal heart rate (60-100) bpm and most of them were found to be experiencing mild stress conditions. The model obtained a classification accuracy of 95 percent (in-sample) and 89.47 percent (out-sample) with a cut off probability value of 0.4.