Vol.12, No.2, May 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                    eISSN: 2217-8333

 

TEM Journal

 

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science


Improving the Avoidant Personality Disorder Prediction for Higher Education Using SMOTE-ENN and Multi-Layer Perceptron Neural Network

 

Sumitra Nuanmeesri, Lap Poomhiran

 

© 2023 Lap Poomhiran, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

 

Citation Information: TEM Journal. Volume 12, Issue 2, Pages 1008-1022, ISSN 2217-8309, DOI: 10.18421/TEM122-47, May 2023.

 

Received: 08 December 2022.

Revised:   14 March 2023.
Accepted:  18 April 2023.
Published: 29 May 2023.

 

Abstract:

 

Adolescents in higher education are more prone to Avoidant Personality Disorder (AVPD), which strongly affects academic achievement. The goal of this study was to create models for accurate prediction of the likelihood of Avoidant Personality Disorder among students in higher education. Information Gain, Gain Ratio, and Wrapper Approach are used as feature selection methods combined with data resampling techniques and machine learning, including Multi-Layer Perceptron Neural Network, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. The findings revealed that the Wrapper approach gave higher accuracy than Information Gain and Gain Ratio approach. Further, using the Gain Ratio approach gives the model a slightly higher efficiency than the Information Gain. Furthermore, when comparing feature selection and data resampling, it was found that the model using feature selection had more higher model efficiency than data resampling alone. Additionally, combining the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbor (SMOTE-ENN) considerably increased the model’s effectiveness. Finally, the model’s efficiency was at its maximum, with an accuracy of 95.52%, when the Wrapper approach was used in conjunction with the Synthetic Minority Over-sampling Technique, the Edited Nearest Neighbor algorithm, and the Multi-Layer Perceptron Neural Network.

 

Keywords – avoidant personality disorder, feature selection, MLPNN, SMOTE-ENN, wrapper.

 

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