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Research on Students' Mental Health Based on Data Mining Algorithms.

Mengjun Luo1

  • 1College of Preschool Education and Humanities, Dongguan Vocational and Technical College, Dongguan, Guangdong 523808, China.

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Summary

This study introduces an intelligent mental health evaluation system using a joint optimization algorithm to improve accuracy and efficiency. The system enhances psychological assessment for students facing societal pressures.

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Area of Science:

  • Psychology
  • Computer Science
  • Artificial Intelligence

Background:

  • Societal development increases psychological pressure on individuals, highlighting the need for robust mental health support.
  • Current mental health intelligence evaluation systems suffer from high misjudgment rates and low efficiency.
  • Strengthening student mental health education is a critical societal concern.

Purpose of the Study:

  • To propose a novel mental health intelligence evaluation system.
  • To address the limitations of existing evaluation methods, specifically misjudgment rates and work efficiency.
  • To enhance the accuracy and efficiency of mental health assessments.

Main Methods:

  • Developed a joint optimization algorithm combining an improved decision tree and an improved Artificial Neural Network (ANN) algorithm.
  • Collected and analyzed mental health intelligence evaluation data using data mining techniques.
  • Employed a joint learning algorithm for data analysis and classification to derive evaluation results.

Main Results:

  • The proposed system demonstrated improved accuracy in mental health intelligence evaluation.
  • The system significantly enhanced the efficiency of the mental health evaluation process.
  • Simulation experiments confirmed the system's feasibility, superiority, and stability over existing methods.

Conclusions:

  • The developed intelligent system effectively overcomes the shortcomings of current mental health evaluation tools.
  • The system offers a stable and accurate solution for mental health intelligence evaluation, meeting current demands.
  • This approach provides a promising advancement in student mental health assessment and support.