Smartphones dependency risk analysis using machine-learning predictive models

Abstract

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.

Figure 1

Automatic system for predicting smartphone dependency.

Figure 2

Information processing flowchart to find out the model.

Figure 3

Receiver Operating Characteristic Curve (ROC curve) for all classification systems.