Fuzzy logic detection of medically serious suicide attempt records in major psychiatric disorders

Ilan Modai*, Julia Kuperman, Imri Goldberg, Michael Goldish, Shalom Mendel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Clinical prediction of suicide is a complicated task. The focus for improved suicide risk detection is on the subgroup of individuals whose high suicide risk remains unrecognized by clinicians. We sought to evaluate the accuracy of Fuzzy Adaptive Learning Control Network (FALCON) neural networks, a nonlinear algorithm, in identification of this subgroup. The study sample included the Computerized Scale for risk of Suicide, including 21 suicide risk factors (including the target variable) drawn from 987 patient records, completed by staff clinicians during face-to-face interviews of hospitalized patients. FALCON evaluated all records in two steps: a) 612 for training and 375 for validation, and b) 887 for training and 100 for validation. The existence of previous medically serious suicide attempts (MSSAs) was chosen as the target variable because it is generally recognized as the strongest suicide risk factor. Sensitivity, specificity, and unknown answers among MSSA and non-MSSA were as follows: 612/375 FALCON, 91%, 85%, 11%, 15%; 887/100 FALCON, 94%, 82%, 20%, 14.5%, respectively. Trained FALCON, a nonlinear neural network, achieves respectable accuracy in detecting MSSA patients based on 20 suicide risk factors. Trained FALCON may therefore assist in identification of subgroup of individuals who remain unrecognized by clinicians and contribute to prevention of suicide.

Original languageEnglish
Pages (from-to)708-710
Number of pages3
JournalJournal of Nervous and Mental Disease
Issue number10
StatePublished - Oct 2004


  • Detection
  • Fuzzy logic
  • Networks
  • Suicide risk


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