ArticleFrontiers in psychiatry2023
Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Social dysfunction in Ecuadorian, Peruvian, and Venezuelan media workers.PLOS mental health · 2026Article
- Development and validation of a machine learning model for post-PCI exercise intolerance in patients with coronary artery disease via electronic medical records.Frontiers in public health · 2026Article
- Global burden and cross-country inequalities in head and neck cancer from 1992 to 2021: results from the global burden of disease study.Health economics review · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
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Authors and funding
5 authors.
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Abstract
In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate's impact on the model's predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model's predictive accuracy, particularly for cases of low social dysfunction.
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