Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Pereira, Telma; Lemos, Luís; Cardoso, Sandra; Silva, Dina; Rodrigues, Ana; Santana, Isabel; de Mendonça, Alexandre; Guerreiro, Manuela; Madeira, Sara C.

doi:10.1186/s12911-017-0497-2

BMC Medical Informatics and Decision Making

Table 5 Results of stratified 10 × 5-fold cross validation with the CV set (patients recruited in Lisbon, Table 3), under the Time Windows and the First Last approaches

From: Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

	AUC					Sensitivity					Specificity
	FL	2Y	3Y	4Y	5Y	FL	2Y	3Y	4Y	5Y	FL	2Y	3Y	4Y	5Y
DT	0.65 ± 0.02	*0.71 ± 0.04*	*0.75 ± 0.01*	*0.78 ± 0.02*	*0.79 ± 0.02*	0.59 ± 0.03	*0.65 ± 0.05*	*0.75 ± 0.04*	*0.71 ± 0.04*	*0.77 ± 0.02*	0.68 ± 0.02	*0.73 ± 0.04*	*0.69 ± 0.03*	*0.77 ± 0.03*	*0.75 ± 0.02*
kNN	0.67 ± 0.01	*0.77 ± 0.01*	*0.82 ± 0.01*	*0.83 ± 0.01*	*0.84 ± 0.01*	0.60 ± 0.02	*0.70 ± 0.03*	*0.87 ± 0.01*	*0.69 ± 0.03*	*0.83 ± 0.01*	0.65 ± 0.01	*0.71 ± 0.01*	0.61 ± 0.01	*0.81 ± 0.02*	*0.72 ± 0.03*
SVM Poly	0.63 ± 0.01	*0.70 ± 0.01*	*0.76 ± 0.01*	*0.79 ± 0.01*	*0.80 ± 0.01*	0.43 ± 0.02	*0.55 ± 0.02*	*0.71 ± 0.01*	*0.81 ± 0.02*	*0.86 ± 0.01*	0.83 ± 0.01	*0.84 ± 0.01*	0.81 ± 0.01	0.77 ± 0.01	0.75 ± 0.02
SVM RBF	0.63 ± 0.01	*0.64 ± 0.01*	*0.76 ± 0.01*	*0.79 ± 0.01*	*0.80 ± 0.02*	0.40 ± 0.02	0.35 ± 0.02	*0.72 ± 0.02*	*0.80 ± 0.02*	*0.89 ± 0.01*	0.86 ± 0.01	*0.93 ± 0.01*	0.81 ± 0.01	0.78 ± 0.02	0.71 ± 0.03
NB	0.74 ± 0.00	*0.82 ± 0.01*	*0.86 ± 0.00*	*0.87 ± 0.01*	*0.88 ± 0.00*	0.64 ± 0.02	*0.66 ± 0.01*	*0.75 ± 0.02*	*0.82 ± 0.01*	*0.88 ± 0.01*	0.71 ± 0.02	*0.82 ± 0.01*	*0.79 ± 0.01*	*0.78 ± 0.01*	*0.71 ± 0.01*
LR	0.72 ± 0.01	*0.79 ± 0.01*	*0.84 ± 0.01*	*0.84 ± 0.01*	*0.85 ± 0.01*	0.47 ± 0.01	*0.77 ± 0.02*	*0.85 ± 0.03*	*0.74 ± 0.01*	*0.78 ± 0.01*	0.80 ± 0.01	0.66 ± 0.01	0.68 ± 0.02	*0.81 ± 0.02*	0.78 ± 0.02
RF	0.72 ± 0.01	*0.79 ± 0.01*	*0.85 ± 0.01*	*0.86 ± 0.01*	*0.87 ± 0.01*	0.59 ± 0.03	0.53 ± 0.04	*0.75 ± 0.01*	*0.75 ± 0.01*	*0.87 ± 0.01*	0.71 ± 0.02	*0.86 ± 0.01*	*0.77 ± 0.02*	*0.81 ± 0.01*	0.70 ± 0.02

Note: DT: Decision Tree classifier, kNN: k-Nearest Neighbor classifier, SVM Poly: polynomial-kernel Support Vector Machines, SVM RB: Gaussian-kernel Support Vector Machines, NB: Naïve Bayes classifier, LR: Logistic Regression and RF: Random Forest
The results were highlighted in bold whenever Time Windows approach outperformed the FL approach. cMCI represents the positive class

Back to article page

ISSN: 1472-6947

Contact us

General enquiries: journalsubmissions@springernature.com