پیش‌بینی سلامت روان دانشجویان با استفاده از مدل تلفیقی مبتنی بر تکنیک‌های داده‌کاوی و تصمیم‌گیری چند معیاره - مطالعه موردی: دانشگاه کوثر بجنورد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، دانشگاه کوثر بجنورد، بجنورد، ایران

2 دکتری مهندسی صنایع، مدرس، گروه مهندسی صنایع، دانشگاه اراک، اراک، ایران.

چکیده
دانشجویان، به‌عنوان متخصصین آینده و ایفاکنندگان نقش‌های مهم در شبکه‌های ارتباطات اجتماعی و توسعه جامعه، با انواع مشکلات و دشواری‌های روانی مواجه هستند. تشخیص زودهنگام و مداخله به‌موقع می‌تواند از بروز مشکلات جدی‌تر جلوگیری نماید. ازاین‌رو این پژوهش با هدف ارائه چارچوبی نوآورانه برای پیش‌بینی سلامت روان دانشجویان، تلفیقی هوشمندانه از تکنیک‌های تصمیم‌گیری چندمعیاره (دیمتل1)، تحلیل مؤلفه‌های اصلی2 بهبودیافته، و یادگیری ماشین را پیشنهاد می‌دهد. ابتدا بر اساس یک مجموعه داده جامع از اطلاعات دانشجویان و با بهره‌گیری از دیمتل، روابط علّی بین عوامل مؤثر بر سلامت روان دانشجویان شناسایی شد؛ سپس با تحلیل مؤلفه‌های اصلی بهبودیافته، متغیر هدف (وضعیت سلامت روان) استخراج گردید. در مرحله مدل‌سازی، بر اساس مقایسه پنج الگوریتم یادگیری ماشین، مدل رگرسیون لجستیک با دقت 100% و قابلیت تفسیرپذیری بالا به‌عنوان مدل برتر انتخاب شد. ترکیب خروجی احتمالی این مدل با سایر الگوریتم‌ها منجر به بهبود دقت همه مدل‌ها به 100% گردید. تحلیل ضرایب نشان داد متغیرهای روانشناختی شامل اضطراب تحصیلی، افکار خودکشی و افسردگی بیشترین تأثیر منفی و متغیرهای وضعیت تأهل و علاقه به رشته اثرات محافظتی بر سلامت روان دارند. نتایج حاصل از این پژوهش می‌تواند به‌عنوان ابزاری قدرتمند جهت شناسایی زودهنگام دانشجویانی که به حمایت‌های روان‌شناختی نیاز دارند، مورداستفاده قرار گیرد.

کلیدواژه‌ها


عنوان مقاله English

Predicting students’ mental health using an integrated model based on data mining and multi-criteria decision-making-A case study: Kosar University of Bojnord

نویسندگان English

Aylin Pakzad 1
Leyla Fazli 2
1 Assistant professor, Department of Industrial Engineering, Kosar University of Bojnord, Bojnord, Iran
2 Ph.D., Lecturer, Department of Industrial Engineering, Arak University, Arak, Iran.
چکیده English

Students, as future professionals and key players in social networks and community development, face various psychological challenges. Early detection and timely intervention can prevent the emergence of more serious issues. Therefore, aiming to provide an innovative framework for predicting students’ mental health, this study proposes an intelligent integration of multi-criteria decision-making (DEMATEL1), improved principal component analysis2, and machine learning techniques. First, based on a comprehensive dataset of student information and using DEMATEL technique, the complex structure of relationships between factors affecting students’ mental health is identified. Then, the target variable (mental health status) was extracted through improved principal component analysis. In the modeling phase, accordingly to comparing five machine learning algorithms, the logistic regression model was selected as the superior model due to its 100% accuracy and high interpretability. Combining the probabilistic output of this model with other algorithms improved all models’ accuracy to 100%. Coefficient analysis revealed that psychological variables-such as academic anxiety, suicidal thoughts, and depression-had the most negative impact, while marital status and interest in the field of study had protective effects on students’ mental health. The results of this study can be used as a powerful tool for early identification of students who need psychological support.

کلیدواژه‌ها English

Prediction
Multi-criteria decision-making
Data mining
Students’ mental health
Modeling
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