Machine Learning-Based Prediction of Cannabis Addiction Using Cognitive Performance and Sleep Quality Evaluations

Cannabis addiction remains a growing public health concern, particularly due to its impact on cognition and sleep quality. Conventional screening tools, such as structured interviews and self-assessments, often lack objectivity and sensitivity. This study aims to develop and compare machine learning (ML) models for the prediction of cannabis addiction using cognitive performance (Montreal Cognitive Assessment – MoCA) and sleep quality (Pittsburgh Sleep Quality Index – PSQI) features. A total of 200 participants aged 13 to 24 were assessed, including 103 diagnosed addicts and 97 controls. Principal Component Analysis (PCA) was used to reduce data dimensionality and enhance model robustness. The study evaluated six supervised machine learning algorithms, namely Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP). Results showed that LR and MLP models achieved high sensitivity (85.71%) and specificity (100%) on the test set, outperforming the DSM-5-based CUD reference test (sensitivity = 71.43%). Although the RF and XGBoost models achieved perfect classification on the training set, their reduced performance on the test set indicates a potential overfitting issue. Integrating machine learning with validated psychometric assessments enables a more accurate and objective identification of cannabis addiction at early stages, thus supporting timely interventions and more effective prevention strategies.

@article{Elhachimi2025,
  title     = {Machine Learning-Based Prediction of Cannabis Addiction Using Cognitive Performance and Sleep Quality Evaluations},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Abdelilah Elhachimi and Mohamed Eddabbah and Abdelhafid Benksim and Hamid Ibanni and Mohamed Cherkaoui},
  doi       = {10.14569/IJACSA.2025.0160439},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160439}
}