Predicting Drug Addiction Using Ensemble Learning: A Majority Voting Classifier Approach
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Information and Communication Technology, Kwara State University, Malete, Nigeria - KWASU Journal of Information, Communication and Technology, 3(1), 17 – 25
Abstract
Globally, drug intake has claimed the lives of half a million people due to its complexity and devastating effects.
Prompt intervention and prevention are crucial to curbing the fatalities and mortality caused by drug addiction.
Leveraging machine learning for drug intake prediction and building on the existing research becomes imperative
to reinforcing the effectiveness of machine learning utilisation, ultimately helping to curb the prevalence of drug
intake. Therefore, this study proposes an ensemble learning approach, using a majority voting classifier to predict
drug addiction from the analysis of facial images. The choice of the majority voting classifier over other ensemble
methods, such as boosting, bagging, and stacking, is due to its simplicity, interpretability, and reduced potential for
overfitting, as it averages out individual model predictions to produce more robust outcomes. A dataset containing
400 facial images was acquired from the Mendeley repository, comprising 240 addicted and 160 non-addicted
images. Combining the predictions of multiple base models, namely ResNet, VGG, and Inception models, this
study’s approach ensures improved accuracy and robustness of the drug addiction prediction model. The Majority
Voting classifier model was evaluated on the acquired dataset, achieving a 0.6573 loss and 69% accuracy rate on
the training set and 2.8133 loss and 60% accuracy on the validation set. The discrepancy between training and
validation performances indicates that the model is overfitting to the training data. As a result, further study can
address the overfitting issue by considering data augmentation to ensure the model performs well in real-world
scenarios.
Keywords: Drug addiction, Facial image analysis, Ensemble learning, Majority Voting classifier, Machine learning