A Machine Learning Approach for Anxiety and Depression Prediction Using PROMIS Questionnaires

A mental disorder is a clinically significant disturbance in an individual's cognition, emotional, or behavioral functioning. Mental disorders such as anxiety and depression can be accessed by psychiatrists using auxiliary tools such as the depression anxiety stress scale (DASS), patient reported outcome (PRO), patient reported outcome measures (PROMs) and patient reported outcomes measurement information system (PROMIS). However, many individuals affected by the symptoms of mental disorders do not receive a proper diagnosis. In that context, this work proposes a machine learning approach to predict the score of anxiety and depression using PROMIS questionnaires by performing a comparative study between supervised learning models to estimate the scores of anxiety and depression from individuals. Through the proposed model an average MAPE of 6.31\\%, R² of 0.76, and Spearman coefficient of 88.86 were achieved, outperforming widely used linear models such as support vector machines (SVM), random forest (RF), and gradient boosting (GB). In conclusion, the utilization of machine learning algorithms with PROMIS questionnaires has shown promise as a methodology for assessing anxiety and depression scores from the participants' perspective, aligning with their perceptions of well-being.

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1. Main Track: Regular Papers

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2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).