Fig. 10

Selection of potential diagnostic biomarkers with machine learning methods. A LASSO regression analysis was applied to screen diagnostic biomarkers based on the 21 intersecting genes in the AP dataset. The genes with the lowest binominal deviance were identified as the most suitable candidates. B The results of the Gini coefficient method for the random forest classifiers in the AP dataset. The x-axis represents genetic variables, and the y-axis represents importance indices. C The number of CDEGs with the lowest error and highest accuracy were considered the most suitable candidates via the SVM-RFE algorithm in the AP dataset. D Venn diagram visualizing the overlap of selected biomarkers between 3 algorithms, yielding 5 genes selected as candidate biomarkers. E LASSO regression analysis was applied to screen diagnostic biomarkers based on the 21 intersecting genes in the BMI>30 dataset. The genes with the lowest binominal deviance were identified as the most suitable candidates. F The results of the Gini coefficient method for the random forest classifiers in the BMI>30 dataset. The x-axis represents genetic variables, and the y-axis represents importance indices in the BMI>30 dataset. G The number of CDEGs with the lowest error and highest accuracy were considered the most suitable candidates via the SVM-RFE algorithm in the AP dataset. H Venn diagram visualizing the overlap of selected biomarkers between 3 algorithms, yielding 4 genes selected as candidate biomarkers