<![CDATA[Mental health disorders pose a significant global challenge, with early identification playing a crucial role in effective intervention and treatment. However, existing diagnostic methods often rely on subjective assessments, leading to potential misdiagnosis and delayed treatment. This study aims to address these limitations by exploring the application of Linear Discriminant Analysis (LDA) for early identification of mental health disorders, specifically focusing on Bipolar Type-1, Bipolar Type-2, Depression, and Normal conditions. Utilizing a publicly available dataset from Kaggle comprising 120 records and 17 attributes, this study applies LDA to classify mental health conditions. The preprocessing steps included handling missing values, encoding categorical data, and normalizing the dataset to enhance model performance. The classification performance was evaluated using a confusion matrix and classification report metrics, demonstrating high accuracy, precision, recall, and F1-scores, particularly for Bipolar Type-1 and Depression, while slightly lower for Bipolar Type-2 and Normal conditions. The novelty of this research lies in the application of LDA to a nuanced mental health dataset, emphasizing its potential as a computational diagnostic tool to complement traditional assessment methods. However, findings suggest that larger, more diverse datasets and the incorporation of objective clinical assessments are necessary to further improve classification accuracy. This study underscores the potential of LDA as a practical and interpretable approach for early mental health diagnosis, providing a foundation for future research to enhance its robustness and clinical applicability.]]>
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