Earticle

초록

영어

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

목차

Abstract
1. Introduction
2. Related research
2.1 Related Concepts of Machine Learning Algorithms
2.2 Related Characteristics of Machine Learning Algorithms
3. Experimental Preparation Research in Anxiety Disorder Prediction Model
3.1 Experimental Method
3.2 Experimental Data Collection
4. Experimental Preparation Research of Anxiety Disorder Prediction Model
4.1 Analysis of the Basic Statistical Data of the Research Sample
4.2 Comparative Analysis of Prediction Experiment Results
5. CONCLUSIONS
Acknowledgement
References

키워드

저자

• Choi DongOun [ Department of Computer Software Engineering, Wonkwang University, Professor ] Corresponding author
• Huan-Meng [ Division of Information, Xiamen University, Professor ]
• Yun-Jeong Kang [ College of Convergence of Liberal, Assisant,Wonkwang University, Professor ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700