Table 3. Metrics about psychometric predicted validity of the Episodix, semantic memory and procedural memory games.

EPISODIX+S+PDATASET			Recall			Precision
ML classifier	F1	k ↓	HC	MCI	AD	HC	MCI	AD
ET: Extra Trees	0,97	0,97	0,96	0,99	0,99	0,99	0,91	1,00
RF: Random Forest	0,97	0,96	0,95	0,98	0,99	0,98	0,91	1,00
GB: Gradient Boosting	0,96	0,95	0,95	0,94	0,98	0,96	0,93	0,98
SVM: Support Vector Machine	0,91	0,90	0,92	0,82	0,99	0,90	0,87	0,98
LR: Logistic Regression	0,84	0,81	0,77	0,80	0,98	0,90	0,67	0,95
AB: Ada Boost	0,84	0,80	0,77	0,78	0,99	0,90	0,70	0,93

Notes.

Episodix, semantic memory and procedural memory dataset (458 triples). ML algorithms: GB, Gradient Boosting classifier; ET, Extra Trees classifier; SVM, Support Vector Machines; LR, Logistic regression; AB, Ada Boost classifier; and RF, Random forest. Metrics: F1 score; Cohen’s Kappa (i.e., used as index to order best classification); recall and precision, the last two distributed by cognitive group. Experiments were performances with cv-fold cross validation (cv = 55) and default configuration in ML algorithms.