Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
SLAS Technol. 2024 Apr;29(2):100129. doi: 10.1016/j.slast.2024.100129. Epub 2024 Mar 18.
Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the "Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the "Liebowitz Social Anxiety Scale questionnaire" and "The fear of speaking in public" are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.
社交焦虑障碍（SAD），也称社交恐惧症，是一种心理疾病，患者会持续且强烈地害怕受到他人的负面评价或关注。这种恐惧会影响他们在工作、人际关系和其他社交活动中的表现。这种精神疾病的发生是多种环境和生物因素的复杂组合导致的。SAD 通过一种名为“精神疾病诊断与统计手册第五版（DSM-5）”的测试进行诊断，该测试基于多种身体、情绪和人口统计学症状。人工智能对医学是一个福音，它经常被用于诊断各种健康状况和疾病。因此，本研究使用人口统计学、情绪和身体症状以及多种机器学习（ML）技术来诊断 SAD。在使用分类器之前，进行了详细的描述性和统计分析。在所有模型中，AdaBoost 和逻辑回归的准确率最高，均为 88%。本研究使用了四种可解释的人工智能技术（XAI），使预测具有可解释性、透明性和可理解性。根据 XAI，“利博维茨社交焦虑量表问卷”和“公众演讲恐惧”是 SAD 诊断中最关键的属性。这个临床决策支持系统框架可以在学校、医院和工作场所等各种合适的地点使用，以识别人们的 SAD。
