<![CDATA[Mental disorders are a growing global health issue, affecting millions of people and significantly impacting quality of life, productivity, and healthcare costs. The WHO reports that over 970 million people worldwide suffer from mental disorders, with rising cases of depression and anxiety during the COVID-19 pandemic. Early detection and accurate classification are crucial for appropriate interventions, yet challenges arise from the complexity and heterogeneity of data. The Random Forest algorithm offers a potential solution through a machine learning approach to classify mental disorders. It excels in handling large, complex datasets and addresses issues of data imbalance. This study aims to develop a mental disorder classification model using Random Forest, which demonstrated superior performance with an accuracy of 88.89%, precision of 90%, recall of 89%, and F1-score of 89%, outperforming five other models. The model is expected to accelerate diagnosis and enhance clinical decision-making in mental disorder management.]]>
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