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This study aims to find the risk factors of Diabetes Mellitus (DM) and to find the best model among Poisson, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regression models. In the count data, the existence of overdispersed data is a common situation for modeling approach. Overdispersion occurs when the data has greater value of variance compared to its mean. The Poisson regression model is a good starting step to model the data but does not account for overdispersion. Hence, NB regression model provide a better way to handle this case. Although it works well, but its inclusion of dispersion parameter seems to increase the probability of zero counts. As a result, we suggest applying ZINB regression model as it capable to handle both overdispersion and excess zeroes. Under the test of Akaike Information Criterion (AIC), Likelihood Ratio (LR), Vuong and Clarke, the ZINB regression model was chosen as the best model. From this analysis, we found that 11 of the parameters were the risk factors that significantly associated with DM at p 0.05. Meanwhile, the respiratory system is the major contributor to the problem of DM.

A Way of Selecting the Right Statistical Model for Handling Overdispersion and Excess Zeroes