An Optimal Decision Tree Model for Diabetes Diagnosis

Diabetes is one of the fastest growing non-communicable diseases in the world. The reason is that the body's ability to produce or respond to insulin is impaired, leading to abnormal metabolism of carbohydrates and elevated levels of diabetes in the blood and urine. This study helped diagnose diabetes by selecting the optimal decision tree model. In order to prevent overfitting of the decision tree model, Expectation-maximization (EM) clustering algorithm is used for data reduction, and then the data is divided into three data sets. The decision tree model is established by different hyperparameters, then the model with the highest accuracy is selected as the optimal model. The model is efficient evaluated by confusion matrix, accuracy, sensitivity and specificity. Compared with other previous studies mentioned in the literature, the proposed model can achieve better accuracy.