Trapped in the Mobile Screen: A Machine Learning Approach to Predict Nomophobia

Modern communication has been revolutionized by the quick spread of cellphones, but an over-reliance on these gadgets has led to a rising psychological ailment called NOMOPHOBIA (NO MObile PHone PhoBIA), which is connected to problems with self-esteem and social anxiety. In this research, nomophobia detection prediction using Machine Learning (ML) techniques is proposed. Four ML models such as Naïve Bayes (NB) Classifier, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) are used to predict the outcome. The hyperparameters of all ML models are tuned using the Random Search technique, and all models are cross-validated using the K-fold cross-validation technique. The performance is evaluated using well-known classification performance evaluation metrics such as accuracy, recall, precision, F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf>, and Geometric Mean (GM). It is found that the SVM outperforms other models.