Modeling the hospitalization time of stroke patients at Abdul Wahab Sjahranie Hospital Samarinda using the Weibull...

The Weibull regression model is a regression model derived from the Weibull distribution, where the Weibull distribution is influenced by covariates. In this study, parameter estimation for the Weibull regression model was conducted using the Maximum Likelihood (ML) estimation. The aim of the study is to develop a Weibull regression model based on the hospitalization time of stroke patients at Abdul Wahab Sjahranie Hospital, Samarinda, during the period of 2021-2022, and to identify the factors affecting it. The event of interest in this study is patient recovery. The results indicate that the ML estimator of the Weibull regression model was obtained numerically using the Newton-Raphson iterative. The factors influencing the Weibull regression model include age, body mass index (BMI), and a history of diabetes mellitus. An increase in patient age and a history of diabetes mellitus are associated with an increase in the probability of the patient not recovering, a decrease in the likelihood of recovery, a lower recovery rate, and a longer recovery time. In contrast, an increase in BMI is associated with a decrease in the probability of the patient not recovering, an increase in the likelihood of recovery, a higher recovery rate, and a shorter recovery time. Some highlights in this article, the proposed method are:•We present The Weibull distribution influenced by covariates is called the Weibull regression model•The potential recovery of stroke disease and the factors that influence it can be analyzed through Weibull regression modeling.•The chance of a patient not recovering is modeled through a Weibull survival regression model, the chance of a patient recovering is modeled through a Weibull cumulative distribution regression model, the patient's recovery rate is modeled through a Weibull hazard regression model, and the average patient hospitalization time is modeled through a Weibull mean regression model.