Bayesian Mixture Statistical Modeling Perspective in the Series of Diabetes Mellitus Disaster Mitigation in Malang Regions

Abstract

Statistical modeling is one of the most important activities in Statistics in order to simplify complex problems in society, to make it easy, simple, and useful. The perspective of statistical modeling is very useful for society in various fields. Probabilistic-based statistical modeling concept is strongly influenced by the shape of the data distribution, data validity, and data availability. Bayesian concept approach in the statistical modeling has advantages compared to the non-Bayesian approach, which is any sample and any distribution of the data and in this case it often occurs in data in the community. In particular, the Bayesian mixture concept discusses the Bayesian approach with data specifications having a mixture (multimodal) distribution. Diabetes Mellitus (DM) is a disease that is not contagious but the side effects are very dangerous for humans and require large costs to handle. Indonesia ranks seventh in the world for the number of DM sufferers and it is estimated that in 2045, the number of DM sufferers in Indonesia will reach approximately 16.7 million people. Mitigation of DM disease in various regions in Indonesia continues to be pursued, including Malang regions. One of the efforts made is through the statistical modeling perspective of the Bayesian approach which can be used for efforts to control, prevent, treat, and overcome DM. The purpose of the study was to build a suitable Bayesian model for DM cases in Malang regions in order to map the DM case areas in Malang. The results showed that in each district area in the city of Malang it was divided into three groups based on the severity of DM sufferers. The three groups are DM sufferers in the categories of not yet severe, moderate, and severe with the model validation indicator using the smallest Kolmogorov-Smirnov value. Sukun District and Klojen District in the Malang region are two districts that need serious attention from the local government of Malang City in dealing with DM cases. Through the perspective of Bayesian statistical modeling, DM cases in five districts in the Malang area showed a mixture distribution with a different number of mixture components as the basis for regional mapping.

Keywords

• bayesian mixture
• diabetes mellitus
• regional mapping
• statistical modeling