Skip to main content
. 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 4.

The diagnostic accuracy of LVQ networks for discrimination of two categories of vasospasm

Number of neurons in Kohonen layer: 8 Number of neurons in Kohonen layer: 16
(a) Vasospasm is categorized as class 1—normal (VA1), and class 2—mild, moderate, or severe [VA(2–3–4)]
VA1-des VA(2–3–4)-des VA1-des VA(2–3–4)-des
VA1-act 0.9469 0.3556 VA1-act 0.9292 0.1778
VA(2–3–4)-act 0.0531 0.6444 VA(2–3–4)-act 0.0708 0.8222
Overall classification rate=0.7957 Overall classification rate=0.8757
(b) Vasospasm is categorized as class 1—normal or mild [VA(1–2)], and class 2—moderate or severe [VA(3–4)]
VA(1–2)-des VA(3–4)-des VA(1–2)-des VA(3–4)-des
VA(1–2)-act 0.9077 0.2143 VA(1–2)-act 0.9846 0.1429
VA(3–4)-act 0.0923 0.7857 VA(3–4)-act 0.0154 0.8571
Overall classification rate=0.8467 Overall classification rate=0.9209
(c) Vasospasm is categorized as class 1—normal, mild, or moderate [VA(1–2–3)], and class 2—severe (V4)
VA(1–2–3)-des VA4-des VA(1–2–3)-des VA4-des
VA(1–2–3)-act 0.9866 0.6667 VA(1–2–3)-act 0.9866 0.2222
VA4-act 0.0134 0.3333 VA4-act 0.0134 0.7778
Overall classification rate=0.6600 Overall classification rate=0.8822

Confusion matrices detail code of actual (act) class (rows) versus code of desired (des) class (columns). All LVQ networks contained three input variables (TCCS velocity data) and two output variables (categories of vasospasm)