<![CDATA[This study evaluates the application of machine learning techniques in improving the prediction and diagnosis of mental health disorders. Traditional diagnostic methods are subjective and time-consuming, necessitating more accurate and efficient alternatives. Using a dataset from the Open-Sourcing Mental Illness survey, this study compares five machine learning algorithms-logistic regression, decision trees, random forests, k-nearest neighbours, and naïve bayes-on mental health prediction tasks. The findings indicate that Naïve Bayes achieves the highest accuracy (82.54%), suggesting its potential for more accurate mental health diagnostics. These results underscore the value of machine learning techniques in enhancing early detection and management of mental health conditions, paving the way for future research into more diverse datasets and ensemble approaches to refine predictive models for clinical application.]]>
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