A GUI-Based tool for social anxiety assessment: A comparative study of machine learning models
Year: 2025
Type: article
Abstract: This paper presents a Graphical User Interface (GUI)-based tool for assessing social anxiety severity using machine learning models. The system is designed to assist individuals who feel uncomfortable engaging with real-life therapists by offering an AI-driven, user-friendly alternative. A structured questionnaire was administered to 500 Jordanian participants, and responses were used to train and evaluate three classification models: Artificial Neural Networks (ANN), Decision Trees (DT), and Random Forests (RF). The dataset was split into 400 training and 100 testing samples. ANN achieved a validation accuracy of 96.5% and test accuracy of 87%. DT reached 98.5% validation and 98% test accuracy. RF outperformed both, achieving 100% accuracy on validation and test sets. These results demonstrate the feasibility of machine learning models in accurately classifying social anxiety levels based on self-reported data. The tool was implemented in MATLAB with an interactive GUI to enhance accessibility and usability. By encouraging users to engage with the assessment process digitally, the system offers a supportive environment for individuals hesitant to seek traditional therapy. This work contributes to the development of AI-assisted mental health tools that are both effective and sensitive to user experience.
Authors: Mohamed W. A. Ramadan, Mallak K. Al-Herbawi, Amir R. Ali
Institutions: University of Jordan, German University in Cairo
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7110904667
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