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ORIGINAL RESEARCH

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

ORCID Icon & ORCID Icon
Pages 2963-2975 | Received 08 Jul 2022, Accepted 13 Sep 2022, Published online: 27 Sep 2022

Figures & data

Table 1 Grid Search Parameter Intervals for Model Training

Figure 1 Flowchart of the model training and test procedure in this study.

Abbreviations: SMOTE, Synthetic Minority Oversampling Technique; RFE, recursive feature elimination; LR, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting; CatBoost, gradient boosting with categorical features support; AUC, area under the curve.
Figure 1 Flowchart of the model training and test procedure in this study.

Figure 2 Distribution of missing features in the training set and test set.

Abbreviations: HGB, haemoglobin; WBC, leukocytes; RBC, erythrocytes; PLT, platelets; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index.
Figure 2 Distribution of missing features in the training set and test set.

Table 2 Feature Comparison Between the Training Set and Test Set- Continuous Variables (Mean± SD)

Table 3 Feature Comparison Between the Training Set and Test Set - Categorical Variables

Figure 3 Violin plot depicting the comparison of basic information between the training set and the external test set.

Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
Figure 3 Violin plot depicting the comparison of basic information between the training set and the external test set.

Table 4 Features Selected by Different Models, Displayed in Descending Order of Importance Percentage

Figure 4 Feature selection for different models by the RFE method. (A) LR; (B) RF; (C) SVM; (D) XGBoost; (E) CatBoost.

Abbreviations: RFE, recursive feature elimination; LR, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting; CatBoost, gradient boosting with categorical features support.
Figure 4 Feature selection for different models by the RFE method. (A) LR; (B) RF; (C) SVM; (D) XGBoost; (E) CatBoost.

Figure 5 ROC curves for the test set.

Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; LR, logistic regression; SVM, support vector machine; RF, random forest; XGBoost, extreme gradient boosting; CatBoost, gradient boosting with categorical features support.
Figure 5 ROC curves for the test set.

Table 5 Evaluation Metrics for the Different Models