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In the modern day, mental health disorder is one of the most dangerous issues in the world. In previously numerous techniques were developed to predict the mental disorders. But, that are failed to predict and classify the process. Therefore, this research a novel Graph convolution-based Spider Word Embedding Fusion (GCbSWEF) model was developed to predict the mental disorders and classify that disorders. This technique includes data collection, pre-processing, feature extraction and selection and finally prediction stages to optimize the performance. Moreover, the developed GCbSWEF model was implemented in Python platform and outcomes are validated with respect to other models. From the validation the developed model has attained better performance for all performance metrics.

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Future Mental Disorder Detection and Prediction of Metal Health Analysis Performance from Social Media Text Data Using Deeper Optimization Strategy

  • 会议论文

  • 发表日期：

    2026年

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  • K. Swapna Rani,
  • K. Maheswari

摘要

In the modern day, mental health disorder is one of the most dangerous issues in the world. In previously numerous techniques were developed to predict the mental disorders. But, that are failed to predict and classify the process. Therefore, this research a novel Graph convolution-based Spider Word Embedding Fusion (GCbSWEF) model was developed to predict the mental disorders and classify that disorders. This technique includes data collection, pre-processing, feature extraction and selection and finally prediction stages to optimize the performance. Moreover, the developed GCbSWEF model was implemented in Python platform and outcomes are validated with respect to other models. From the validation the developed model has attained better performance for all performance metrics.