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<p>Mental disorders, such as stress, anxiety, and depression, represent significant challenges to individual well-being. Traditional assessment methods often fall short of capturing the complexities of these conditions, particularly within multifaceted treatment contexts. Therefore, there is a pressing need for innovative approaches to effectively address issues related to mental health disorders. Recent advancements in artificial intelligence present an opportunity to develop novel methodologies for this purpose. This study introduces a new Convolutional Neural Network (CNN) algorithm, termed Gaussian CNN, aimed at enhancing the detection of mental health disorders. The experimental results demonstrate that the Gaussian CNN achieves a training accuracy of 0.9642 and a testing accuracy of 0.9638, along with superior performance in evaluation metrics, such as accuracy, precision, recall, and F1 score. These findings suggest that the proposed technique holds promise as an effective solution for the detection of mental health conditions.</p>

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Mentalix: stepping up mental health disorder detection using Gaussian CNN algorithm

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Wahyu Rochdiat Murdhiono,
  • Herliana Riska,
  • Nur Khasanah,
  • Hamzah,
  • Putra Wanda

摘要

Mental disorders, such as stress, anxiety, and depression, represent significant challenges to individual well-being. Traditional assessment methods often fall short of capturing the complexities of these conditions, particularly within multifaceted treatment contexts. Therefore, there is a pressing need for innovative approaches to effectively address issues related to mental health disorders. Recent advancements in artificial intelligence present an opportunity to develop novel methodologies for this purpose. This study introduces a new Convolutional Neural Network (CNN) algorithm, termed Gaussian CNN, aimed at enhancing the detection of mental health disorders. The experimental results demonstrate that the Gaussian CNN achieves a training accuracy of 0.9642 and a testing accuracy of 0.9638, along with superior performance in evaluation metrics, such as accuracy, precision, recall, and F1 score. These findings suggest that the proposed technique holds promise as an effective solution for the detection of mental health conditions.