The screening of cannabis addiction using machine learning, MoCA, and anxiety/depression tests
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Notice bibliographique
Résumé
A new cannabis addiction screening approach is developed based on the use of Machine Learning (ML) alongside with psychological and cognitive assessment tests. The Hospital Anxiety and Depression (HAD) test is used to determine the Absence of Anxiety and Depression “No Disorder” or the presence of “Anxiety Only”, “Depression Only”, and both “Anxiety and Depression”. These features are considered to evaluate the psychological effects of cannabis use. In addition to that, the Montreal Cognitive Assessment (MoCA) test is chosen as a feature to assess the cognitive state of the participants. Also, the Age of First Cannabis Use (AFCU) is included as a feature to incorporate participants with no prior cannabis use alongside with those who have used cannabis before. The portion of participants with prior experience consuming cannabis comprises a subset containing passive users and a subset of users that have developed cannabis addiction. This 6-month study was conducted in Marrakech, Morocco in a center affiliated to the National Association of Drug-Risk Reduction of Morocco known as RdR-Maroc with the participation of 146 subjects. The participants in this study are grouped into two groups of 73 participants each. Both case and control groups were clinically assessed by a professional. In this paper, the classical statistical data analysis for the AFCU (M=15.6; SD=2.4), MoCA (M=25.0; SD=2.4), and HAD (58 cases with no disorder, 88 cases with disorder) failed to separate between the two classes. Also, the feature selection for modeling is performed using Spearman correlation, Kendall correlation, and the Odds Ratio (OR). These statistics showed that the selected features were associated to addiction except for “Anxiety Only”. Moreover, ML models such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), k th-Nearest Neighbor (KNN), and Decision Tree (DT) were used to classify participants into two categories (Addict vs. Non-Addict) based on the selected features mentioned earlier. The Receiver Operating Characteristic (ROC) curve is used to overview the quality of the models and showed that they all captured the nonlinearities underlined in the data. On the other hand, the Area Under the Curve (AUC) allowed the ranking of these models and indicated that the SVM model outperformed all other models with an AUC of 0.97. After that, the SVM model was compared to the Cannabis Use Disorder (CUD) based screening method issued by the fifth edition of the Diagnostic and Statistical Manual of mental disorders (DSM-5). The SVM model's accuracy of 96.6 % outperformed the CUD questionnaires accuracy of 87.7 %. The validity of the proposed approach was investigated where the SVM model presented high validity characteristics (sensitivity=1, specificity=0.93) compared to the CUD questionnaire (sensitivity=0.75, specificity=1). Hence, the proposed approach provided satisfactory results compared to current adopted CUD based screening test. Our findings expand current research on the development of simple and powerful tools for cannabis addiction screening with the purpose of early detection in order to establish preventive and therapeutic strategies that could potentially mitigate the prevalence of cannabis addiction.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle