Correlating Clinical Assessments for Substance Use Disorder Using Unsupervised Machine Learning

This paper investigates the state of substance use disorder (SUD) and the frequency of substance use by utilizing three unsupervised machine learning techniques, based on the Diagnostic and Statistical Manual 5 (DSM-5) of mental health disorders. We used data obtained from the National Survey on Drug Use and Health (NSDUH) 2019 database with random participants who had undergone clinical assessments by mental health professionals and whose clinical diagnoses are not known. This approach classifies SUD status by discovering patterns or correlations from the trained model. Our results are analyzed and confirmed by a mental health professional. The three unsupervised machine learning techniques that we used are k-means clustering, hierarchical clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) applied to alcohol, marijuana, and cocaine datasets. The clustering results were validated using the silhouette score, k-fold cross-validation, and root mean squared error (RMSE). The results from this study may be used to supplement psychiatric evaluations.