Recognizing Social Anxiety in High School Students

This study investigates the prevalence and impact of social anxiety among high school students at Little Scholars Matriculation Hr. Sec. School in Thanjavur, Tamil Nadu, India. A dataset was created by surveying students with a 17-item Social Phobia Inventory (SPIN) questionnaire, which includes questions related to their experiences with social interactions, fear of judgment, and discomfort in various social situations. Using this dataset, the research applies a Random Forest machine learning approach to analyze student responses and assess the severity of social anxiety. The model aims to predict the levels of social anxiety by identifying significant features that contribute to higher distress levels. Through feature selection and correlation analysis, the study uncovers complex relationships between various aspects of social interactions that influence social anxiety. The performance of the Random Forest model is evaluated based on its accuracy and predictive power, demonstrating its ability to predict social anxiety in high school students effectively. The study highlights the potential of Random Forest for accurately identifying key factors associated with social phobia and recommends further research to refine predictive models, offering valuable insights for enhancing mental health support systems for high school students