Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Koushik, S.; Basha, Md Shaik Amzad; Sucharitha, M Martha; Ayesha, Samreen Title A name given to the resource Screen Time to Severity: Machine Learning Models for Teen Smartphone Dependency Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 4th International Conference for Advancement in Technology, ICONAT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONAT66879.2025.11362619" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT66879.2025.11362619</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033915070?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033915070?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Koushik S., Christ (Deemed to be University), Computer Science and Engineering (AI/ML), Bengaluru, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India; Sucharitha M.M., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Ayesha S., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India Description An account of the resource This study presents a systematic comparison of fourteen supervised classifiers trained to predict binned smartphone addiction levels (Low/Medium/High) in a cohort of 300 teenagers, using demographic, usage, academic, and health related features. After cleaning and binning the continuous Addiction_Level score into three categories, we encoded all categorical variables and standardized inputs, then stratified into 80 % training and 20 % test splits. Our expanded model suite comprised: Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Support Vector Machine, and a multilayer perceptron (MLP). Each classifier was evaluated on accuracy, precision, recall, macro-averaged F1-score, and multiclass ROC AUC; confusion-matrix entries were flattened into nine 'Actual_i to Pred_j' columns per model for granular error analysis. Logistic Regression achieved the highest test accuracy (98.83%) , outstanding ROC AUC (0.9982) and perfect precision in discriminating the majority class ('High' addiction), despite modest recall for minority classes. MLP followed (96.33 % accuracy, 0.9878 AUC), indicating that a shallow neural network can capture nonlinear patterns but struggles on underrepresented labels. Gradient Boosting, CatBoost, and LightGBM all exceeded 95% accuracy with strong F1-scores (?0.72-0.73) and AUCs above 0.96, demonstrating the power of tree-based ensembles on mixed data types. Simpler methods (e.g., GaussianNB, KNN, Decision Tree) performed moderately (86-91% accuracy, AUC 0.84-0.98), while AdaBoost lagged (77.5 % accuracy, AUC 0.867), suggesting sensitivity to noisy features. Confusion-matrix summaries revealed that most models rarely misclassify Low-addiction teens, but confusion arises between Medium and High classes important for targeted interventions. 2025 IEEE. Subject The topic of the resource Adolescents; Digital Well-being; Ensemble Methods; Machine Learning; Multiclass Classification; Predictive Modeling; Smartphone Addiction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159573-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online