Transforming Mental Healthcare: Deep Learning for Accurate Diagnosis and Personalized Treatment Recommendation

Accurate diagnosis and effective treatment recommendations for mental disorders remain high on the list of challenging issues in healthcare in terms of precise diagnosis and effective treatment. Human accuracy in diagnosing remains low, and traditional machine learning techniques are still yielding suboptimal results when applied to medical claims data for depression screening. An attempt is made to bridge this limitation through a multi-layer neural network that might be trained on raw medical data, picking up on hidden patterns without explicit feature selection. The integrated web-based platform was developed using bagging techniques of Convolutional Neural Networks (CNN), Bidirectional Encoder Representations from Transformers (BERT), and Long-Short-Term-Memory (LSTM) networks for mental disorder detection with more robustness. The input the users give enhances both the validity of diagnosis and prescribed treatments to deal with mental disorder conditions. Research goes into significantly improving the mental disorder prediction accuracy using precision and personalization that diagnostics should entail, relying on deep learning to fill the apparent holes in healthcare practices. The platform enables clinicians to make more informed decisions and builds less dependency on traditional time-consuming manual assessments. It can eventually increase good-quality access to mental health care in those underserved regions. Ultimately, this approach works toward making mental health treatment more accessible, effective, and individualized for each person’s needs.