Table 3.

Model performance of 24 classification algorithms in training data set (model for functional connectivity combined with the ApoE4 genotype).

Algorithm	Accuracy	Recall	Precision	F1-score
Fine tree	0.70	0.71	0.69	0.70
Medium tree	0.70	0.70	0.69	0.69
Coarse tree	0.64	0.61	0.65	0.63
Linear discriminant	0.79	0.61	0.95	0.74
Logistic regression	0.62	0.30	0.82	0.44
Kernel Naïve Bayes	0.70	0.70	0.69	0.69
Linear SVM	0.66	0.64	0.67	0.65
Quadratic SVM	0.75	0.67	0.80	0.73
Cubic SVM	0.75	0.57	0.88	0.69
Fine KNN	0.80	0.66	0.93	0.77
Medium KNN	0.66	0.43	0.81	0.56
Coarse KNN	0.49	0.79	0.49	0.60
Cosine KNN	0.64	0.87	0.60	0.71
Cubic KNN	0.71	0.48	0.91	0.63
Weighted KNN	0.71	0.41	1.00	0.58
Boosted trees	0.49	0.79	0.49	0.60
Bagged trees	0.76	0.72	0.79	0.75
Subspace discriminant	0.75	0.61	0.84	0.71
Subspace KNN	0.78	0.62	0.90	0.73
Narrow neural network	0.81	0.74	0.87	0.80
Medium neural network	0.83	0.72	0.92	0.81
Wide neural network	0.84	0.74	0.92	0.82
Bilayered neural network	0.75	0.69	0.79	0.74
Trilayered neural network	0.75	0.59	0.86	0.70

The bold values represent the best-performing algorithm among the 24 classification algorithms in the training data set (model for functional connectivity combined with the APOE4 genotype).

ApoE4, apolipoprotein E4; KNN, K-nearest neighbor; SVM, support vector machine.