TY - JOUR
T1 - Family-wise automatic classification in schizophrenia
AU - Mandl, R.C.W.
AU - Brouwer, R.M.
AU - Cahn, W.
AU - Kahn, R.S.
AU - Hulshoff Pol, H.E.
PY - 2013/9
Y1 - 2013/9
N2 - Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component. © 2013 Elsevier B.V.
AB - Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component. © 2013 Elsevier B.V.
UR - https://www.scopus.com/pages/publications/84881223729
UR - https://www.scopus.com/inward/citedby.url?scp=84881223729&partnerID=8YFLogxK
U2 - 10.1016/j.schres.2013.07.002
DO - 10.1016/j.schres.2013.07.002
M3 - Article
SN - 0920-9964
VL - 149
SP - 108
EP - 111
JO - Schizophrenia Research
JF - Schizophrenia Research
IS - 1-3
ER -