Citation

Delgado-Gomez D, Blasco-Fontecilla H, Sukno F, Socorro Ramos-Plasencia M, Baca-Garcia E. Neurocomputing 2012; 92: 3-8.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.neucom.2011.08.033

PMID

unavailable

Abstract

Suicide is a major public health issue with considerable human and economic cost. Previous attempts to delineate techniques capable of accurately predicting suicidal behavior proved unsuccessful. This paper aims at classifying suicide attempters (SA) as a first step toward the development of predictive models of suicidal behavior. A sample of 883 adults (347 SA and 536 non-SA) admitted to two university hospitals in Madrid, Spain, between 1999 and 2003 was used. Five multivariate techniques (linear regression, stepwise linear regression, decision trees, Lars-en and support vector machines) were compared with regard to their capacity to accurately classify SA. These techniques were applied to the Holmes-Rahe social readjustment rating scale and the international personal disorder examination screening questionnaire. Combining both scales, the Lars-en and stepwise linear regression techniques achieved 83.6% and 82.3% classification accuracy, respectively. In addition, these classification results were obtained using less than half of the available items. Multivariate techniques demonstrated to be useful in classifying SA using a combination of life events and personality criteria with reasonable accuracy, sensitivity and specificity. 2012 Elsevier B.V..