Diabetes prediction based on artificial intelligence

Abstract:

Objective To establish a diabetes prediction model based on four classifiers of extreme gradient boosting（XGBoost），light gradient boosting machine（LightGBM），adaptive boosting（AdaBoost），and multilayer perceptron（MLP） according to clinical indicators，and to evaluate the screening effect.Methods According to the case-control study design，99 attributes of clinical data from the study group and the control group were collected，and analyzed by python 3.8. Then the linear interpolation method and an inherent non-negative latent feature（INLF） model were used to predict the feature missing value，and the classification model was constructed using four classifiers to detect diabetes.Results Through analyses of 3 241 patients with hypertension combined with diabetes（study group） and 4 181 patients with hypertension（control group） in the model，99 features were included. The accuracy rates of the diabetes classification model based on XGBoost，LightGBM，AdaBoost，and MLP classifiers were 0.894 9，0.887 5，0.862 0，and 0.856 6，respectively.Conclusion Our proposed classifier model framework based on INLF prediction has a good screening effect，and preliminarily solves the problem of early diabetes screening through machine learning，which has certain practical significance for clinical diagnosis and can be used as a simple and effective screening method for diabetes and its complications.