Classification of diabetes disease using TCM electronic nose signals and ensemble learning

Diabetes is one of the most prevalent diseases in medical field. We propose an ensemble method for diagnosis of diabetes on traditional Chinese medicine electronic nose signals. To evaluate the effectiveness of our method, we carry out the experiments by comparing single classifier with ensemble classifiers based on support vector machine and logistic classification model. The proposed method shows better classification performance with accuracy of 88.04%. The results of this study show that ensemble method is effective to detect diabetes.