Pls toolbox r8

Different subgroups of auto-scaled data were assessed for GDM prediction through five classification ML techniques: logistic regression (LR), linear support vector machine (L-SVM), partial least squares discriminant analysis (PLS-DA), classification and regression tree (CART) and extreme gradient boosting (XGB). Every model was subjected to leave-one-out cross-validation. LR, PLS-DA and XGBoost were executed in PLS_Toolbox R8.9.2 (Eigenvector Research Inc, USA). L-SVM and CART were implemented by coding in MATLAB R2021a (The MathWorks Inc, USA).
The classification predictive performance was assessed by the determination of the models sensitivity (Se), specificity (Sp) and non-error rate (NER) in both calibration and cross-validation. These parameters were calculated as follows:
$$Se\left(\%\right)=\frac{TP}{TP+FN}\bullet 100$$
$$Sp\left(\%\right)=\frac{TN}{TN+FP}\bullet 100$$
$$NER\left(\%\right)=\frac{Se+Sp}{2}$$
Where TP, FN, TN and FP are the number of true positives, false negatives, true negatives and false positives, respectively.
The area under the receiver operating characteristic curve (AUC) of the final classification models was determined through the software GraphPad Prism 9.2.0 (GraphPad Software Inc, USA).

Different subgroups of auto-scaled data were assessed for post load glycemia prediction through the regression ML technique partial least squares (PLS). Every model was subjected to leave-one-out cross-validation. PLS was executed in PLS_Toolbox R8.9.2 (Eigenvector Research Inc, USA).
The regression predictive performance was assessed by the determination of the models root mean square error (RMSE) and relative error (RE) in both calibration (RMSEC and REC) and cross validation (RMSECV and RECV). These parameters were calculated as follows:
$$RMSEC=\sqrt{\frac{{\sum }_{i=1}^n{(\widehat{y_i}-y_i)}^2}{n-1}}$$
$$RMSECV=\sqrt{\frac{{\sum }_{i=1}^n{(\widehat{y_i}-y_i)}^2}{n}}$$
$$REC\left(\%\right)=\frac{RMSEC}{\overline{y}}\bullet 100$$
$$RECV\left(\%\right)=\frac{RMSECV}{\overline{y}}\bullet 100$$
Where ŷ_i is the predicted post load glycemia for subject i, y_i is the actual post load glycemia for subject i, n is the number of subjects in calibration and cross-validation, and ȳ is the mean of the actual post load glycemia values in calibration.
The Spearman r correlation coefficient of the final regression models was determined by the software GraphPad Prism 9.2.0 (GraphPad Software Inc, USA).