Advanced International Journal for Research

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Clustering Analysis using Joint Dirichilet Mixture Model

Author(s) Dr. Gangaraju Lakshmi Kameswari
Country India
Abstract Diabetes is a major public health challenge in India and worldwide, with increasing prevalence and diverse risk patterns across populations. Traditional statistical methods, such as histograms, kernel density estimation, and maximum likelihood estimation, often fail to fully capture the complexity of diabetes-related data. These methods typically assume fixed parametric forms or provide limited uncertainty quantification, which restricts their applicability in clinical and epidemiological decision-making. Recent studies have demonstrated that Bayesian approaches can overcome these limitations by allowing flexible estimation of probability density functions (PDFs), integrating prior knowledge, and explicitly quantifying uncertainty through posterior distributions. In particular, Dirichlet Process Mixture Models (DPMMs) and Bayesian networks have been successfully applied to estimate joint distributions of glycaemic markers, risk factors, and complications in diabetes datasets. However, despite global advances, there is limited application of Bayesian density estimation techniques to Indian diabetes data, where missing values, heterogeneous risk factors, and regional variation present additional challenges. In this paper the authors have attempted to model PIMA Indian Diabetes data set using Joint DPMM and dynamic clustering of the data is performed and tabulated. It is reported that out of available 768 rows of data, they are segregated into 03 significant clusters out of 12 dynamic clusters obtained and emphirical relations of glucose levels interms of the other variables such as Age, Sex, pregnancies, BMI, Insulin and Skin thickness with diabetes pedigree function is presented for prediction and forecasting.
Keywords Joint Dirichilet Mixture model, Diabetes, PIMA Dataset, Bayesian nonparametric tool.
Field Mathematics
Published In Volume 6, Issue 5, September-October 2025
Published On 2025-10-14
DOI https://doi.org/10.63363/aijfr.2025.v06i05.1539
Short DOI https://doi.org/g97nxb
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