KMAN Publication Inc. (KMANPUB) Journal of Psychosociological Research in Family and Culture 3041-8550 4 Serial Number 14 2026 04 01 Machine Learning-Based Classification of Cultural Beliefs and Mental Health Attitudes Machine Learning-Based Classification of Cultural Beliefs and Mental Health Attitudes 1 11 EN Journal Article 2025 07 13 <table> <tbody> <tr> <td> <p><strong>Objective: </strong>The present study aimed to classify cultural beliefs and mental health attitudes using machine learning techniques and to identify the most influential psychosocial predictors within an Armenian population.</p> <p><strong>Methods and Materials: </strong>This cross-sectional, descriptive–analytical study was conducted on a sample of 412 adults from Armenia selected through stratified random sampling. Data were collected using standardized instruments assessing cultural beliefs (including individualism, collectivism, traditionalism, and authority orientation) and mental health attitudes (including stigma, help-seeking attitudes, emotional openness, and beliefs about mental illness), along with a demographic questionnaire. Data preprocessing included normalization, missing data imputation, and categorical encoding. Feature selection was performed using correlation-based filtering and recursive feature elimination. Supervised machine learning models, including Support Vector Machine, Random Forest, and Gradient Boosting, were implemented to classify participants based on their psychosocial profiles. Model evaluation was conducted using 10-fold cross-validation, with performance assessed via accuracy, precision, recall, F1-score, and AUC-ROC metrics.</p> <p><strong>Findings: </strong>Inferential results indicated significant associations between cultural belief dimensions and mental health attitudes, with collectivism, traditionalism, and authority orientation positively predicting mental health stigma, while individualism significantly predicted help-seeking attitudes and emotional openness. Machine learning analyses revealed that the Gradient Boosting model demonstrated superior classification performance (accuracy = 0.902, AUC = 0.947) compared to Random Forest and Support Vector Machine models. Feature importance analysis showed that mental health stigma, help-seeking attitudes, and collectivism were the most influential predictors in distinguishing participant profiles. Correlation patterns further confirmed that higher stigma was significantly associated with lower help-seeking and emotional openness, while culturally embedded belief systems significantly shaped mental health perceptions.</p> <p><strong>Conclusion: </strong>The findings demonstrate that cultural beliefs play a critical role in shaping mental health attitudes and that machine learning techniques provide a robust framework for accurately classifying these complex relationships. The prominence of stigma-related constructs highlights the need for culturally sensitive interventions aimed at reducing stigma and promoting adaptive help-seeking behaviors. Integrating computational approaches with cultural psychology offers valuable insights for targeted mental health strategies and policy development.</p> </td> </tr> </tbody> </table> https://www.journals.kmanpub.com/index.php/jprfc/article/download/5443/9873