///Tab 4

Research Article

Machine Learning for the Preliminary Diagnosis of Dementia

Table 4

Overall performance of the diagnostic models.


Algorithm	特征选择	Accuracy	Precision	Recall	F-measure

Random Forest	Relief	0.78	0.80	0.78	0.78
	Information Gain	0.78	0.79	0.78	0.78
	Random Forest	0.76	0.77	0.76	0.76

AdaBoost	Relief	0.77	0.78	0.77	0.77
	Information Gain	0.77	0.78	0.77	0.77
	Random Forest	0.76	0.76	0.76	0.76

LogitBoost	Relief	0.80	0.75	0.80	0.76
	Information Gain	0.78	0.73	0.78	0.74
	Random Forest	0.76	0.77	0.76	0.74

MLP	Relief	0.81	0.75	0.81	0.77
	Information Gain	0.79	0.73	0.79	0.75
	Random Forest	0.78	0.76	0.78	0.76

朴素贝叶斯	Relief	0.79	0.74	0.79	0.75
	Information Gain	0.81	0.82	0.81	0.81
	Random Forest	0.77	0.80	0.77	0.78

SVM	Relief	0.80	0.74	0.80	0.76
	Information Gain	0.79	0.73	0.79	0.75
	Random Forest	0.76	0.74	0.76	0.75

Results were obtained after using the feature selection.

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