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. 2023 Dec 2;16:3901–3913. doi: 10.2147/DMSO.S439127

Figure 3.

Figure 3

Comparative Analysis of ML Classifiers across Different Data Types. (A–C) reveal the performance (ROC, calibration, and DCA curves) of ML classifiers (Logit, SVM, RF, and XGBoost) applied to clinical data, with ROC-AUCs of 0.70, 0.66, 0.65, and 0.69, respectively. (D–F) show these classifiers’ performance based on radiomics features, yielding AUCs of 0.93, 0.92, 0.89, and 0.90. (G–I) display their performance when applied to combined clinical and radiomics data, with AUCs reaching up to 0.94, 0.95, 0.87, and 0.91. While the combined data models demonstrate the highest AUC values, a noticeable decrement in calibration is observed, highlighting the superior suitability of radiomics-based models for evaluating fibrosis progression risk in T2DM patients with NAFLD. Among these, SVM demonstrates superior discrimination and calibration, and comparable DCA curve performance, designating it as the most effective classifier.