Aplikasi Pemodelan Logit, Probit dan Clog-Log Pada Regresi Binomial (Studi Kasus: Pemodelan Penyakit Jantung)

Abstract

This study aims to compare the modeling of the causative factors of heart disease using logit, probit and clog-log regression. The three models gave the same results for both the simultaneous and partial tests in modeling the probability case of heart disease. The Probit model gives the best results with the smallest error value criteria (AIC and BIC) and the largest prediction accuracy value. All complications of disease (high blood pressure, cholesterol, diabetes, smoking habits) have a higher chance of developing heart disease risk than those who do not. In terms of gender, men have a higher risk of heart disease than women. In general, it can be concluded that complications and gender have an effect on heart disease. Based on these results, it is expected to increase awareness of the risk of heart disease and its causes.