Fig. 5. Summary of the Machine learning algorithm performance in predicting the cognitive status of the participants based on the MRI-segmented brain volume and thickness and age.

A Schematics of XGB classifier ML model based on the gradient boosting technique. B Cognitive status prediction accuracy of different ML Models for combination of different MRI obtains neuroanatomic volumes and thicknesses. MRI features were added one by one with age and gender to check for the increase in average accuracy of XGB classifier (blue circle), Random Forest (green triangle), Bagging classifier (yellow diamond) and Simple Classification Tree (orange square) ML models. The result showed the mean accuracy ± SD (standard deviation). The highest accuracy for all ML models was obtained for the combination of three MRI features, i.e., total Brain volume, CSF, and WMH with age and gender, and out of the 4 ML models, the XGB classifier gave the highest accuracy. C Average normalized confusion matrix for XGB classifier ML machine models for predicting the cognitive status of the test data for the three optimized MRI features (total brain volume, CSF, and WMH) with age and gender, which gave the highest accuracy. *BRNV total brain volume, CSF cerebrospinal fluid, LV lateral ventricle, HP hippocampus, WMH white matter hyperintensity, EC entorhinal cortex and PHG parahippocampal gyrus.