Prediction of insulin resistance by statistical tool MARS

Örsçelik, Simge Gökçe
Recently, following the rise in obesity prevalence, the incidence of type 2 diabetes rose remarkably. Diabetes is a serious disorder, accompanied by increased risk of developing heart disease, kidney failure, and new cases of blindness. Dietary habits are strongly related to type 2 diabetes. We sought to observe how dietary protein and glycemic index patterns, weight change and/or other predictors we selected relate to insulin resistance change. First, we applied multiple linear regression, and then statistical tool Multivariate Adaptive Regression Splines (MARS) to a clinical data set. Refining the settings, we selected an optimal model. It constituted a good prediction for our problem. According to our results, weight change strongly relates to insulin resistance change. Moreover, weight change and baseline insulin resistance are highly interacting with each other. Together, they have a strong effect on the model performance. Similarly, we observed an interaction between weight change and dietary protein content. Weight change and dietary protein jointly relate to insulin resistance change. Yet we could not detect any relationship between dietary glycemic index and insulin resistance change. The thesis ends with a conclusion and an outlook to future studies.


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Citation Formats
S. G. Örsçelik, “Prediction of insulin resistance by statistical tool MARS,” M.S. - Master of Science, Middle East Technical University, 2014.