Prediction of type-2 diabetes based on several element levels in blood and chemometrics.

The present study was designed to evaluate the levels of eight elements including lithium, zinc, chromium, copper, iron, manganese, nickel and vanadium in whole blood of type-2 diabetes patients, to compare them with age-matched healthy controls and to investigate the feasibility of combining them with an ensemble model for diagnosing purpose. A dataset involving 158 samples, among which 105 were taken from healthy adults and the remaining 53 from patients with type-2 diabetes, was collected. All samples were split into the training set and the test set with the equal size. Based on a simple variable selection, two elements, i.e., chromium and iron, are also picked out as the most important elements. Three kinds of algorithms, i.e., fisher linear discriminate analysis (FLDA), support vector machine (SVM) and decision tree (DT), were used for constructing member models. The best ensemble classifiers constructed on the training set were validated on the independent test set, and the prediction results were compared with those from clinical diagnostics on the same subjects. The results reveal that almost all ensemble classifiers exhibit similar performance, implying that these elements coupled with an appropriate ensemble classifier can serve as a valuable tool of diagnosing diabetes type-2.