Anuradha Kumari Singh  S. Karthikeyan

Vol. 2,  No. 1, pp. 1-19, Jan.  2025
10.23246/AAIRJ.2025.02.01.05

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The rising dropout rate in universities poses a significant challenge for educational institutions. This study aims to identify the reasons behind student dropouts at universities. We utilized a dataset of undergraduates from 17 different courses, encompassing 36 attributes. To achieve maximum classification accuracy, we proposed a wrapper feature selection technique to identify the most important subset of features. Our approach employs the Whale Optimization Algorithm combined with a neural network model (WOA-NN). In this study, we compared the performance of this hybrid approach with six different machine learning algorithms (Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors) and a standalone neural network model. Based on the results of our experiments, we recommend the WOA-NN wrapper technique for feature selection. The main objective of the feature selection process is to identify the smallest subset of features that achieves maximum classification accuracy. We trained the neural network model using both the full set of features and the selected subset. The performance of the neural network model with the selected features was significantly better than with the full feature set, resulting in an improvement in accuracy from 92.13\% to 96.28\%. This enhanced accuracy enabled us to identify key factors contributing to student dropout in higher education.