Screening of anxiety and depression among seafarers using machine learning technology

Seafarers are vulnerable to suffering from various mental health disorders, most commonly anxiety and depression. Therefore, periodic screening for anxiety and depression is necessary for their health and well-being. Machine learning technology may be useful as a rapid, automated screening procedure to identify at risk seafarers for early referral to psychological counselling and treatment. To compare the performance of machine learning algorithms for screening of anxiety and depression among seafarers. A total 470 seafarers were interviewed at Haldia Dock Complex, India, after obtaining necessary permissions and ethical clearance. Various socio-demographic, occupational, and health-related information were collected. Then presence of anxiety and depression was assessed by the Hospital Anxiety and Depression Scale. Five machine learning classifiers i.e., CatBoost, Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine, were evaluated using the Python programming language. CatBoost appeared to be the most satisfactory measure for this purpose, with an accuracy and precision 82.6% and 84.1%, respectively. This study emphasizes the application of machine learning technology in the field of automated screening for mental health illness. Using this technology, a manually derived and time consuming screening procedure for anxiety and depression can be replaced by an automated computer based analysis technique with a degree of accuracy sufficient for screening purposes.