Hans Adey Cesa, Joshua Daniel Lawsin, Carlos Julian Chiongbian, Maria Angelica Zaragoza, Glenn Pepito, Rhoda Alvarez
Social Anxiety Disorder is one of the most commonly diagnosed anxiety disorder that can disrupt a person’s work and social life. People with social anxiety disorder experience the symptoms of fear and anxiety in situations where there is a possibility of being scrutinized or judged by others. Clinicians conduct clinical assessment through clinical interviews, screening and diagnostic testing when the patient seeks professional help. However, the process usually takes two to four hours to complete to avoid false positive impressions leading to misdiagnosis. Thus, this paper proposes a novel approach to streamline the screening process without compromising the accuracy rate of predicting a possible manifestation of social anxiety disorder within patients through an intelligent web-based screening tool that uses established machine learning algorithms to screen and evaluate a patient. The tool assesses the possibility of SAD based on the information gathered from the patient’s demographic, physiological symptoms, affective stability, and feared situations. Four machine learning models namely: Decision Tree, Logistic Regression, Support Vector Machine, and K-Nearest Neighbours, were trained, tested, and cross-validated. Using K-Fold Cross-Validation, we evaluated four machine learning models based on accuracy, precision, recall, f1, and AUC – ROC curve. The SVM model performed the best among the other models and garnered the highest accuracy of 96.01%, with 97.13% precision, 95.33% recall, 96.13% f1 score, and an AUC score of 0.97 ± 0.05. The Support Vector Machine model is then integrated into the developed screening tool. © School of Engineering, Taylor’s University.
Department of Computer, Information Sciences, and Mathematics, School of Arts and Sciences, University of San Carlos, Talamban Campus, Cebu City, 6000, Philippines; Department of Psychology, School of Arts and Sciences, University of San Carlos, Talamban Campus, Cebu City, 6000, Philippines
