# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 5 Discussion  
This research employed four distinct machine learning techniques to estimate the average duration of internet usage, utilising data spanning from 2014 to 2024, sourced from the World Bank. To enhance the model's reliability and precision, a cross-validation approach was implemented. The findings revealed that all four models effectively predicted the average time spent online. Evaluation metrics, including the R\({}^{2}\), MSE, MAE, and MAPE, were calculated to assess the models' performance. Notably, the ANN emerged as the most successful model, achieving an R\({}^{2}\) of 92.5%, MSE of 0.001, MAE of 0.016, and MAPE of 0.043. The ANN model outperformed the SVR, RF, and time series methods. Consistent with the existing literature, the study demonstrated that the ANN was the most effective technique. Furthermore, the results indicated that the model provided predictions with greater accuracy and reduced errors compared to previous studies in the literature. The relationship between internet usage and phubbing is well documented in prior studies [10]. Increased time spent online correlates with reduced face-to-face interaction quality, thereby escalating phubbing behaviours. This study bridges this gap by forecasting future internet usage patterns and discussing their potential impact on phubbing's prevalence. Our findings highlight the importance of addressing phubbing through predictive modelling to mitigate its social and psychological consequences. Although the artificial neural network (ANN) provided the highest accuracy rate among the models used in this study, the advantages and disadvantages of other models for different scenarios should be discussed. For example, the random forest (RF) model may be faster and more effective on small datasets with its less complex structure, while support vector regression (SVR) may give better results on non-linear data. The success of a time series model depends on the characteristics, seasonality, and non-linearity of the data, so the advantages of different models should be assessed for specific datasets. In future studies, it is suggested to evaluate the performance of more advanced models (e.g., LSTM and XGBoost) on complex datasets. These comparisons will be useful to determine which model is more appropriate in different contexts. Technological developments in smartphones increase phubbing behaviours by offering individuals factors such as the internet, social media, and games together. This situation causes people to focus on their smartphones instead of communicating with their surroundings in social environments and negatively affects their social communication. There are many studies indicating that internet use has a negative impact on individuals' mental health [64; 65; 66; 67; 68; 69]. Therefore, considering that the duration of internet usage will increase by almost 1.5 times, the physical and social problems that may be experienced in the future will be directly related to the increasing duration of internet usage. The results of this study are consistent with prior research on the predictive capabilities of machine learning models for internet usage. For instance, the ANN's superior performance in terms of accuracy aligns with findings from similar studies [10; 22]. However, our study differs by focusing on the social implications of these predictions, particularly their relevance to phubbing behaviours, a topic rarely explored in the existing literature. The phubbing phenomenon should be addressed not only with quantitative analyses but also in a qualitative context. For example, interview or survey studies can be conducted to understand individuals' experiences and perceptions of this phenomenon. Furthermore, case studies examining the effects of phubbing on family relationships, work environments, and social interactions may allow for a broader perspective on this phenomenon. Such qualitative studies would deepen the meaning of existing findings and contribute to providing more comprehensive solutions.