Authors: Singareddy Saritha, M. Sivaparavathi
Abstract: A rising number of individuals are displaying signs such as excessive phone usage, loss of productivity, and even physical and psychological health concerns, making Smartphone addiction a major worry in recent years. The development of reliable instruments for the prediction of Smartphone addiction and the identification of those at risk is, hence, necessary. Using survey data on Smartphone use, we constructed a machine learning model to forecast Smartphone addiction in this research. There was a wide variety of mental health concerns addressed in the survey, including demographics, phone use patterns, and anxiety, despairs, and stress. The model was constructed using a well-liked and efficient machine learning technique. In this work, numerical variables are normalized and categorical variables are encoded as part of the data preprocessing to make sure the model can train properly. Also, we used measures like accuracy to measure the model's performance on the remaining data after training it on a subset of the data. The algorithm has successfully predicted Smartphone addiction with a high degree of accuracy, according to the findings. Use habits of mobile phones, including how often notifications were checked, how many hours spent on the phone daily, and the applications used most often, were the most critical variables for predicting addiction. Age, gender, and stress levels were other important factors. The constructed model has a number of possible uses. Healthcare providers might use it to identify patients at risk of Smartphone addiction and intervene accordingly. Also, app makers may utilize it to make their applications less addicting and more conducive to healthy phone habits. In a nutshell, the results show that machine learning algorithms can effectively predict Smartphone addiction. We need to conduct further studies to confirm our results on bigger and more varied datasets and to investigate other possible uses for this approach.
DOI: http://doi.org/10.5281/zenodo.20286754
