Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

Figures

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  Figure 1. A graphical view of the complete research work in this study, including patient enrollment; demographic, clinical, and laboratory information acquisition; machine learning modeling; and performance evaluation of the model.

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  Figure 2. The proposed procedure for screening diabetic patients. Every patient initially goes through the information acquisition of this clinical diagnosis flowchart. Five tests are then applied in two stages. The second stage (multiclass class is only for the patients who go through the CAN test and have a positive CAN. A single-class classification can predict the presence of microvascular complications (CAN, DPN, or RET) and can predict whether there is any presence of complications. Multiple complications with CAN could be classified using the multiclass classifier.

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  Figure 3. (a) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (b) confusion matrix of the CAN test (pClass vs. nClass); (c) performance evaluation matrices; (d) TPR vs. FPR, graphical view of the CAN classifier model performance.

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  Figure 4. (a) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (b) confusion matrix of the DPN test (pClass vs. nClass); (c) performance evaluation matrices; (d) TPR vs. FPR, graphical view of the DPN classifier model performance.

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  Figure 5. (a) Chi-squared test result. The importance of different marked features was used in the model as an identifier; (b) confusion matrix of the RET test (pClass vs. nClass); (c) performance evaluation matrices; (d) TPR vs. FPR, graphical view of the RET classifier model performance.

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  Figure 6. Performance comparison (confusion matrix) between the multiclass SVM classifier and the multiclass RF classifier. (a) Confusion matrix of SVM classifier; (b) confusion matrix of RF classifier; classes: 1. CAN, patients with CAN; 2. CANDPN, with DPN and CAN; 3. CANDPN+, patients with NEP and/or RET with CAN and DPN.