The Effect of Dataset Limitation on the Diabetes Risk Factors

Diabetes research is an important topic in the medical domain. However, the research on diabetes risk factors always restricts objects to a certain category. This research wants to figure out the effect of such limitations. The result of this study could give guidance in this field. A dataset built by the Center of Disease Control and Prevention, which has been cleaned is used in this study. The binomial logistic regression analysis is applied to this research and the decision tree model is used for testing. The results suggest that many environmental factors are associated with diabetes. However, taking these risk factors together did not accurately predict diabetes. By analyzing and comparing, limitations on the dataset could increase the accuracy of prediction. The result states that limitations during the study on diabetes risk factors could lead to high-accuracy correlation results. This study provides a good guide for other research on diabetes risk factors to investigate people with more common points in their daily lifestyle.