Predicting Drug Addiction Using Ensemble Learning: A Majority Voting Classifier Approach

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dc.contributor.authorAkuji, I.I.
dc.contributor.authorOgunrinde, M.A.
dc.contributor.authorAhmed, T.O.
dc.contributor.authorAbdulsalam, S.O.
dc.contributor.authorBabatunde R.S.
dc.date.accessioned2025-10-29T12:16:32Z
dc.date.available2025-10-29T12:16:32Z
dc.date.issued2024
dc.description.abstractGlobally, 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
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6299
dc.language.isoen
dc.publisherFaculty of Information and Communication Technology, Kwara State University, Malete, Nigeria - KWASU Journal of Information, Communication and Technology, 3(1), 17 – 25
dc.titlePredicting Drug Addiction Using Ensemble Learning: A Majority Voting Classifier Approach
dc.typeArticle
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