Application of machine learning techniques for multi-class classification performance on mental disorders

The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health. Consequently, governmental and non-governmental groups allocate considerable resources to tackle these concerns. The precise identification of such illnesses via artificial intelligence techniques has become critically significant. These methodologies, widely used across several healthcare sectors, have been increasingly adopted in psychiatry. Evaluating psychiatric data to forecast disease accurately enables doctors to make better decisions and establishes a robust decision-support framework. This work employed prevalent machine learning techniques to identify mental illnesses utilizing a dataset with labels for seven distinct mental states—six disorders and one normative condition. The approaches were initially evaluated on an imbalanced dataset and subsequently on a balanced dataset, facilitating comparison of the outcomes. Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.