T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

Table 2 Comparative analysis of machine learning modeling of radiomics

	AUC	95%CI	Sensitivity	Specificity	Accuracy	PPV	PPV
Training cohort
LR	0.737	0.656-0.818	0.527	0.875	0.577	0.961	0.239
SVM	0.986	0.973-0.999	0.947	1.000	0.955	1.000	0.762
KNN	0.880	0.835-0.924	0.649	1.000	0.700	1.000	0.327
RF	1.000	0.999-1.000	0.989	1.000	0.991	1.000	0.941
ET	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
XGBoost	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
LightGBM	0.972	0.953-0.992	0.910	0.969	0.918	0.994	0.646
MLP	0.796	0.723-0.869	0.660	0.812	0.682	0.954	0.289
Validation cohort
LR	0.728	0.586-0.870	0.692	0.765	0.577	0.931	0.351
SVM	0.684	0.527-0.841	0.756	0.588	0.955	0.894	0.345
KNN	0.629	0.485-0.772	0.628	1.000	0.700	0.875	0.256
RF	0.597	0.442-0.752	0.872	0.417	0.991	0.850	0.333
ET	0.620	0.497-0.743	0.423	1.000	1.000	0.943	0.250
XGBoost	0.594	0.430-0.758	0.808	0.471	1.000	0.875	0.348
LightGBM	0.601	0.464-0.739	0.372	0.882	0.918	0.935	0.234
MLP	0.735	0.604-0.866	0.641	0.824	0.682	0.943	0.333

AUC: Area under the curve; ET: Extra trees; LightGBM: Light gradient boosting machine; KNN: K-nearest neighbor; LR: Logistic regression; MLP: Multilayer perceptron; NPV: Negative predictive value; PPV: Positive predictive value; RF: Random forest; SVM: Support vector machine; XGBoost: Extreme gradient boosting.