<![CDATA[Mental health conditions present a complex diagnostic challenge due to the subtlety and diversity of symptoms. This study provides a comprehensive analysis of various neural network architectures, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory networks (LSTM), and Dense Neural Network (DNN), in their ability to classify mental health conditions. Utilizing a rich dataset of symptoms and expert diagnoses, we preprocessed the data to address class imbalances and trained each model to evaluate its diagnostic performance. Our results are presented through confusion matrices that reveal the accuracy, precision, recall, and F1-scores for each model. The MLP and DNN models demonstrated high accuracy in identifying distinct conditions but struggled with overlapping symptoms. LSTM and RNN models captured temporal patterns to some extent yet required further optimization for improved accuracy. CNN models showed robust feature detection capabilities, with the CNN 1D model excelling in specificity for certain conditions. However, a common challenge across all models was the differentiation between conditions with similar symptom presentations. Our findings suggest that while individual models have their strengths, an ensemble approach may be necessary for enhanced diagnostic precision. Future work will focus on integrating models, refining feature extraction, and employing explainable AI to increase transparency and trust in model predictions. Additionally, expanding the dataset and conducting clinical trials will ensure the models' effectiveness in real-world settings. This research moves us closer to achieving nuanced, AI-driven diagnostics that can support clinicians and benefit patient outcomes in mental healthcare.]]>
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