Review of Machine Learning Algorithms for Diagnosing Mental Illness

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment

OBJECTIVE: This study evaluated the psychometric properties of the Korean Anxiety Screening Assessment (K-ANX) developed for screening anxiety disorders. METHODS: Data from 613 participants were analyzed. The K-ANX was evaluated for reliability using Cronbach’s alpha, item-total correlation, and test information curve, and for validity using focus group interviews, factor analysis, correlational analysis, and item characteristics based on item response theory (IRT). The diagnostic sensitivity and specificity of the K-ANX were compared with those of the Beck Anxiety Inventory (BAI) and Generalized Anxiety Disorder 7-item scale (GAD-7). RESULTS: The K-ANX showed excellent internal consistency (α=0.97) and item-total coefficients (0.92–0.97), and a one-factor structure was suggested. All items were highly correlated with the total scores of the BAI, GAD-7, and Penn State Worry Questionnaire. IRT analysis indicated the K-ANX was most informative as a screening tool for anxiety disorders at the range between 0.8 and 1.6 (i.e., top 21.2 to 5.5 percentiles). Higher sensitivity (0.795) and specificity (0.937) for identifying anxiety disorders were observed in the K-ANX compared to the BAI and GAD-7. CONCLUSION: The K-ANX is a reliable and valid measure to screen anxiety disorders in a Korean sample, with greater sensitivity and specificity than current measures of anxiety symptoms.