A prediction model for peer attachment in KOREAN female adolescents using back propagation neural network


  • Haewon Byeon
  • . .






Datamining, Back Propagation Neural Network, Peer Attachment, Risk Factors, Female Students.


Background/Objectives: This study used data mining technique to explore the potential factors affecting the peer attachment of South Korean female students.

Methods/Statistical analysis: This study analyzed 2009 9th grade female students, who attended Panel Study on Korean Children in 2016. Peer attachment was defined as a dependent variable. The explanatory variables included gender, academic achievement satisfaction, subjective household economy level, parent-child dialogue frequency, subjective health status, depression symptom, self-esteem, subjective life satisfaction, and mobile phone dependence. The predictors of peer attachment were analyzed by using back propagation neural network (BPN).

Findings: Analysis results showed that depression, self-esteem, dialogue level between parent and child regarding school life, subjective health condition were highly related to the peer attachment of female students.

Improvements/Applications: It is required to develop a customized educational program to form a successful social relationship between adolescent female students.




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How to Cite

Byeon, H., & ., . (2018). A prediction model for peer attachment in KOREAN female adolescents using back propagation neural network. International Journal of Engineering & Technology, 7(2.33), 27–30. https://doi.org/10.14419/ijet.v7i2.33.13847
Received 2018-06-08
Accepted 2018-06-08
Published 2018-06-08