MMSE prediction AUC and accuracy performance for ADNI dataset.

Top panel: Area under the ROC curve (AUC), Bottom panel: Accuracy. Results are stratified by groups defined by the clinical workflow. Abbreviations are as follows. BL: baseline visit, CA: clinical attributes, CT: cortical thickness. BE: baseline edge-cases, FE: follow-up edge-cases, CC: cognitively consistent. The statistical comparison of LSN against other models was performed using Mann-Whitney-U test. Note that only BL+followup, CA+CT input is relevant for this comparison. For AUC comparison, LSN offered significantly better performance over all four models for ‘All’, ‘BE’, and ‘CC’ subsets. LSN also offered statistically significant results over RF and ANN models for ‘FE’ subset. For accuracy comparison, LSN offered significantly better performance over SVM, RF, and ANN models for ‘All’, ‘FE’, and ‘CC’ subsets. No statistically significant results were obtained for ‘BE’ subset.