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The rising frequency of mental health illnesses worldwide requires improved diagnosis methods for prompt and appropriate treatment. Diagnostic methods that rely on subjective judgments are limited by physician knowledge and biases. This study performs literature survey of related works and identifies a few research gaps. Further, it introduces a Hybrid Deep Learning Model (HDLM) to improve mental health diagnosis accuracy. The HDLM uses a Convolutional Neural Network (CNN) to analyze and interpret complicated patient data patterns and a Recurrent Neural Network (RNN) to process and learn from sequential data to comprehend temporal dynamics in symptom development. The HDLM uses electronic health information, patient self-reports, and physiological signs to find subtle patterns and connections that physicians may miss. Attention processes help the suggested model concentrate on crucial mental health problem signs for a more accurate diagnosis. The results extensively exhibit that the advantages of RNN and CNN gives a considerable increase in diagnostic accuracy and reliability compared to older approaches. The HDLM may help physicians make better judgments and provide a more objective, data-driven approach to mental healthcare. This research identifies that hybrid deep learning may enhance mental health diagnosis, personalizing therapy and improving results.

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RNN-CNN Based Hybrid Deep Learning Model for Mental Healthcare

  • 会议论文

  • 发表日期：

    2025年

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  • Sonali Chopra,
  • Parul Agarwal,
  • Jawed Ahmed,
  • Siddhartha Sankar Biswas,
  • Ahmed J. Obaid

摘要

The rising frequency of mental health illnesses worldwide requires improved diagnosis methods for prompt and appropriate treatment. Diagnostic methods that rely on subjective judgments are limited by physician knowledge and biases. This study performs literature survey of related works and identifies a few research gaps. Further, it introduces a Hybrid Deep Learning Model (HDLM) to improve mental health diagnosis accuracy. The HDLM uses a Convolutional Neural Network (CNN) to analyze and interpret complicated patient data patterns and a Recurrent Neural Network (RNN) to process and learn from sequential data to comprehend temporal dynamics in symptom development. The HDLM uses electronic health information, patient self-reports, and physiological signs to find subtle patterns and connections that physicians may miss. Attention processes help the suggested model concentrate on crucial mental health problem signs for a more accurate diagnosis. The results extensively exhibit that the advantages of RNN and CNN gives a considerable increase in diagnostic accuracy and reliability compared to older approaches. The HDLM may help physicians make better judgments and provide a more objective, data-driven approach to mental healthcare. This research identifies that hybrid deep learning may enhance mental health diagnosis, personalizing therapy and improving results.