Predicting mental health treatment outcomes using machine learning: Insights from a global survey dataset

The issue of mental health is still the major public health problem around the world, and it is strong evidence of the necessity of data-driven approaches for early detection and treatment. The current research applies machine-learning techniques to ascertain the treatment-seeking behavior of the patients through a dataset of survey responses that can be accessed publicly and carried out in different countries like the U.S., Canada, and the U.K. The descriptive analyses demonstrated significant differences in the levels of stress reported, indoor confinement issues, and gender differences in treatment-seeking behavior. People experiencing higher stress and longer indoor durations were more likely to seek treatment, whereas females showed higher treatment rates than males. Logistic Regression and Random Forest are two classification models that were built and assessed in order to foretell the treatment results. The Random Forest model was the most accurate one with an accuracy of 0.73, precision ranging from 0.72 to 0.74, and recall from 0.70 to 0.76, better than Logistic Regression (accuracy = 0.70). Feature importance analysis revealed growing stress, days spent indoors, and family history as the most influential factors in the decision to seek mental health treatment. The results indicate that machine learning can thoroughly identify the risk patterns related to behavioral and demographic factors for mental health conditions. The research adds to the group of mental health studies with computer-based methods and shows the possible role of predictive analytics in promoting proactive well-being strategies and helping with focused interventions.