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Leveraging AI to Analyze Factors Relating to Social Anxiety

Neha K.

Our research aims to leverage ML to understand what factors play the most critical role in the presence of social anxiety disorder in a person.

With our changing lifestyles and the rise of technology, there has been a steady rise of reported social anxiety cases throughout the years. Exploring how different factors contribute to social anxiety disorder may help people understand significant causes in a cost effective and relatively reliably way. Our research aims to leverage ML to understand what factors play the most critical role in the presence of social anxiety disorder in a person. Additionally, through training different models, we aim to determine the capability of AI in predicting one’s social anxiety. To analyze the factors relating to social anxiety, we clustered our data to find relationships within the background data (e.g: family history, age, gender, symptoms) and one’s social anxiety diagnosis. Our findings suggest that fears and physical conditions may contribute more to social anxiety than a person’s history/background.

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Neha K.
Udgam Goyal
MEng CS from MIT, Product Manager at Aurora and prior AI Product Management at Microsoft

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Research Paper