Validation of the Revised Stressful Life Event Questionnaire Using a Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Abstract

Objectives. 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. Conclusion. 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.

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“…death of a loved one, serious illness, poverty in childhood, or physical abuse in childhood). Following Sali and colleagues the amount and the weighted quality of stressful life events were computed to a total score (SLE-S) [ 65 ].…”

Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data

“…13,14 Questionnaires have been used to study musculoskeletal, psychological, cardiovascular, and other disorders, [15][16][17][18][19][20][21] and as the popularity of questionnaires has grown, so has the potential for new understanding via advanced statistical techniques such as GA. Although GA has been successfully applied to questionnaire variable selection in the context of family medicine, stressful life events, 22 and sleep apnea diagnosis, 23 its application to the selection of questionnaire data for predictive modeling of disease outcome is relatively novel. Outside biomedical research, Madden utilized GAs in the analysis of questionnaire data to ascertain students' attitudes toward their schoolwork, showing that GAs may be used to generate logical rules, which predict one variable in relation to others.…”