title: Validation of the revised stressful life event questionnaire using a hybrid model of genetic algorithm and artificial neural networks.
abstract: OBJECTIVE
  Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE) questionnaire by adding weights to the events in order to measure and determine a cut point.
  
  
  METHODS
  A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12). A hybrid model of genetic algorithm (GA) and artificial neural networks (ANNs) was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale) and the GHQ score as a response variable. In this model, GA is used in order to set some parameter of ANN for achieving more accurate results.
  
  
  RESULTS
  For each stressful life event, the number is defined as weight. Among all stressful life events, death of parents, spouse, or siblings is the most important and impactful stressor in the studied population. Sensitivity of 83% and specificity of 81% were obtained for the cut point 100.
  
  
  CONCLUSIONS
  The SLE-revised (SLE-R) questionnaire despite simplicity is a high-performance screening tool for investigating the stress level of life events and its management in both community and primary care settings. The SLE-R questionnaire is user-friendly and easy to be self-administered. This questionnaire allows the individuals to be aware of their own health status.
authors.affiliation: Industrial Engineering Department, Sharif University of Technology, P.O. Box 11365-9466, Tehran, Iran.
authors.forename: Rasoul
authors.initials: R
authors.lastname: Sali
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Roohafza
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Sadeghi
authors.forename: Elham
authors.initials: E
authors.lastname: Andalib
authors.forename: Hassan
authors.initials: H
authors.lastname: Shavandi
authors.forename: Nizal
authors.initials: N
authors.lastname: Sarrafzadegan
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D008957:Models, Genetic; D016571:Neural Networks, Computer; D015995:Prevalence; D011594:Psychometrics; D013315:Stress, Psychological / Q000175:diagnosis*; D011795:Surveys and Questionnaires; D055815:Young Adult