Ani Budi Astuti, Nur Iriawan, Irhamah, Heri Kuswanto, Laksmi Sasiarini
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. © Published under licence by IOP Publishing Ltd.
Department of Mathematics, Statistics Study Program, Faculty of Mathematics and Natural Sciences, University of Brawijaya Malang, Indonesia; Department of Statistics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; Faculty of Medicine, University of Brawijaya Malang, Indonesia
