Leveraging AI to Analyze Factors Relating to Social Anxiety

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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Height Prediction Using Basic Data

I used basic data sets to see if some learning models have a chance of predicting height based on country and age.

Daniel S.

Mentored by Jonathan Delgadillo Lorenzo

Computational Approaches to Enhance Idiopathic Intracranial Hypertension Diagnosis: A Neural Network-Based Framework for Improved Clinical Decision Support

A computer model, trained on eye images, can tell if optic nerves are healthy or not with 97% accuracy. This is important because it messes up less often than people do, making it a useful tool. The model looks at pictures of the back of the eye (fundus imaging) to make these predictions.