Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta

Abstract

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

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“…During the model training, the cross-entropy cost function was used to adapt weights during the learning process by minimizing the loss; and the optimal model hyperparameters were tuned by grid search algorithm and evaluated using 10-fold cross-validation. In addition, to fix the group imbalance in our dataset, the best way could be to generate additional samples for minority classes, which means that the model performance to correctly predict the minority class label is getting better by using SMOTE-ENN to balance our data ( 24 ). The SMOTE-ENN combination resampling method strategically integrates the advantages of the SMOTE technique and the Edited Nearest Neighbor (ENN) algorithm.…”

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“…(a) SMOTE-ENN combines the SMOTE and edited nearest neighbor (ENN) techniques and is assigned to the hybrid sampling technique group [183]. SMOTE is an oversampling technique, which generates synthetic samples for the minority class.…”

“…In order to overcome these disadvantages, the ENN technique is used, which removes samples from both classes [186]. The ENN algorithm can be described as a data cleaning method, which may remove any sample whose class label is different from the class of two or more of its closest neighbors [183]. (b) SMOTE-TL is a hybrid technique, which combines SMOTE and the Tomek links (TL) techniques.…”