Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Abstract

Abstract. Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept. IntroductionWhen a set of real data observed have a very small amount and a multimodal distribution then it is necessary need specially handling. The Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. These approach is based directly on the posterior distribution of the data. Therefore Bayesian approach can accommodate the real condition of the data based on the data driven concept and [5]). Bayesian Mixture Model (BMM) is one model that uses a Bayesian approach for multimodal models and its parameter models is seen as random variables in the model parameter space [6].Various studies have previously been carried out by BMM modeling. The modeling of BMM for the DM data in Surabaya is the research that has been done by [7] and the research of [8] has been conducted for the DM data in Malang by BMMA modeling. Some of the BMM research in the other field, among others [9] and [10]. Modeling of cases is very important in order to know the exact model and can be used for decision making in accordance with the cases observed [11].

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

“…In various previous studies, many researchers have carried out DM disease, two of which are Suharjo's research in 2003 as research reference Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) which examined DM cases in Surabaya where the distribution of DM case data was proven to have a normal mixture distribution. Furthermore, research by Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) carried out statistical modeling with a normal Bayesian mixture approach for DM cases in the Malang city area in various districts.…”

“…In various previous studies, many researchers have carried out DM disease, two of which are Suharjo's research in 2003 as research reference Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) which examined DM cases in Surabaya where the distribution of DM case data was proven to have a normal mixture distribution. Furthermore, research by Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) carried out statistical modeling with a normal Bayesian mixture approach for DM cases in the Malang city area in various districts. The results of research by Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) also show that the distribution of DM case data in each of the five districts in Malang city follows the normal mixture distribution and the form of the normal mixture distribution varies between regions one district to another.…”

“…Furthermore, research by Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) carried out statistical modeling with a normal Bayesian mixture approach for DM cases in the Malang city area in various districts. The results of research by Astuti et al (2015a) ; Astuti et al (2015b) ; Astuti et al (2016) ; Astuti et al (2017) also show that the distribution of DM case data in each of the five districts in Malang city follows the normal mixture distribution and the form of the normal mixture distribution varies between regions one district to another. Based on the normal mixture model obtained, it can be used to determine the condition of each district in the Malang city area for DM cases.…”