Background: Machine learning techniques allow highly accurate prediction of different tasks by measuring the event probabilities. This research proposes a prediction model for dependency on smartphones based on machine learning techniques. Methods: We performed an analytical observational study with a retrospective case–control approach; the different classification methods used were decision tree, random forest, logistic regression, and support vector machine. The sample demographic included 1228 students from a private university in Cali. The tests were 1) smartphone dependency assessment and 2) the Nordic musculoskeletal symptoms questionnaire. Results: It was found that some of the variables related to smartphone dependency are academic curriculum, school, marital status, socioeconomic status, rules, discussions, and discrimination. Conclusions: The support vector machine model evidences highest prediction precision for smartphone dependency, obtained through the stratified-k-fold cross-validation technique.
Recent technological advances have changed how people interact, run businesses, learn, and use their free time. The advantages and facilities provided by electronic devices have played a major role. On the other hand, extensive use of such technology also has adverse effects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dependency is new addiction that primarily affects the young population. The consequences may negatively impact mental and physical health (e.g., lack of attention or local pain). Health professionals rely on self-reported subjective information to assess the dependency level, requiring specialists' opinions to diagnose such a dependency. This study proposes a data-driven prediction model for smartphone dependency based on machine learning techniques using an analytical retrospective case–control approach. Different classification methods were applied, including classical and modern machine learning models. Students from a private university in Cali—Colombia (n = 1228) were tested for (i) smartphone dependency, (ii) musculoskeletal symptoms, and (iii) the Risk Factors Questionnaire. Random forest, logistic regression, and support vector machine-based classifiers exhibited the highest prediction accuracy, 76–77%, for smartphone dependency, estimated through the stratified-k-fold cross-validation technique. Results showed that self-reported information provides insight into predicting smartphone dependency correctly. Such an approach opens doors for future research aiming to include objective measures to increase accuracy and help to reduce the negative consequences of this new addiction form.
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