Anishka Duvvuri, Navya Kovvuri, Sneka Kumar, Rebecca Victor, Tanush Kaushik, Basis Independent Silicon Valley High school, USA

Anxiety is a chronic illness especially during the Covid and post-pandemic era. It’s important to diagnose anxiety in its early stages. Traditional Machine learning (ML) methods have been developmental intense procedures to detect mental health issues, but Automated machine learning (AutoML) is a method whereby the novice user can build a model to detect a phenomenon such as Generalized Anxiety Disorder (GAD) fairly easily. In this study we evaluate a popular AutoML technique with recent chat engine (Discord) conversation dataset using anxiety hashtags. This multi-symptom AutoML Random Forest predictive model is at least 75+% accurate with the most prevalent symptom, namely restlessness. This could be a very useful first step in diagnosing GAD by medical professionals and their less skilled hospital’s IT area using pre diagnostic textual conversations. But it lacks high quality in predicting GAD in most symptoms as found by a low 50% precision on most symptoms (except 5). The AutoML technology is quicker for IT professionals and gives a decent performance, but it can be improved upon by more sophisticated ANN methods like Convolution neural networks that plug AutoML’s symptom’s deficiencies with at least 80+% precision and 0.4+% in F1 score, namely in detecting poorly predicted symptoms of concentration and irritability.

General Anxiety Disorder, machine learning, Discord chat. AutoML, Convolutional neural network.