. Author manuscript; available in PMC: 2009 Oct 9.

Published in final edited form as: Neuroinformatics. 2008 Aug 13;6(4):279–290. doi: 10.1007/s12021-008-9023-0

Table 2.

The diagnostic accuracy of LVQ networks for discrimination of four categories of vasospasm increases with the number of competitive neurons

	VA1-des	VA2-des	VA3-des	VA4-des
Number of neurons in Kohonen layer: 8
VA1-act	0.7168	0.3529	0.1053	0.2222
VA2-act	0.2478	0.5882	0.0526	0.0000
VA3-act	0.0354	0.0588	0.7368	0.2222
VA4-act	0.0000	0.0000	0.1053	0.5556
Overall classification rate=0.6494
Number of neurons in Kohonen layer: 16
VA1-act	0.8496	0.3529	0.2105	0.1111
VA2-act	0.0442	0.5294	0.0000	0.0000
VA3-act	0.0256	0.1176	0.7368	0.1111
VA4-act	0.0796	0.0000	0.0526	0.7778
Overall classification rate=0.7234

Vasospasm is categorized as class 1—normal (VA1), class 2—mild (VA2), class 3—moderate (VA3), and class 4—severe (VA4). All LVQ networks contained three input variables (TCCS velocity data) and four output variables (categories of vasospasm). Confusion matrices detail code of actual (act) class (rows) versus code of desired (des) class (columns)