# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 4 Results and Discussion  
This study utilised data from the World Bank, spanning 2014 to 2024. Within the model developed for the study, the average time spent on the internet served as the dependent variable. Four distinct machine learning techniques were employed: an ANN, RF regression, SVR, and time series. The model's performance and error rate were evaluated using statistical metrics such as the R\({}^{2}\), MSE, MAE, and MAPE. Figure 1 shows the fundamental working structure of the study. Within the domain of data science, an attribute refers to each column within a dataset, serving as a crucial element in producing the desired model output. Attribute selection, or feature selection, encompasses the process of identifying and selecting the most valuable attributes from the dataset. This step is pivotal in influencing the efficacy and efficiency of a machine learning model. Including extraneous attributes can lead to prolonged training times and may compromise the model's simplicity and transparency. Furthermore, an excess of attributes can diminish the interpretability of the model, complicating its analysis and understanding [47]. Overfitting can lead to a model performing well on the training dataset but poorly on a test dataset. This discrepancy arises when the records in the test dataset differ significantly from those in the training set, resulting in a high error rate. Feature selection methods are typically categorised into three main types: filter methods, wrapper methods, and embedded methods. Filter methods evaluate the importance of features by analysing their relationship with the target variable. This process filters the dataset, producing a subset that includes only the most relevant features [48]. In this study, we employed the Pearson correlation method, a type of filter method, to assess the degree of relationship between variables that had a linear connection. Within the scope of the study, the number of cellular mobile network users, the average literacy rate of the population, the female literacy rate, the male literacy rate, and the mobile (cell) phone ownership rate of the population were excluded from the independent variables because they were below the threshold value determined according to the Pearson correlation method. Figure 1: Structure of the study. Normalisation is a technique employed to rectify inconsistencies that arise when data are collected using varying scales or coding systems, thereby ensuring the preservation of the data's integrity [49]. In this study, the dataset underwent normalisation using the min-max method. This technique involves applying a linear transformation to the original data, typically confining them within the range of [0-1]. In this context, the minimum value represents the lowest possible value that a data point can assume, while the maximum value indicates the highest. During the min-max normalisation process, the method evaluates the extent to which each data point exceeds the minimum value and ranks these differences accordingly [50]. Equation (2) shows min-max normalisation:  
\[\mathrm{x_{new}}=\frac{\mathrm{x-x_{min}}}{\mathrm{x_{max}-x_{min}}} \tag{2}\]  
Cross-validation is a popular validation technique employed in data analysis methods like machine learning and statistical modelling. Its primary aim is to ensure that the dataset is utilised effectively when assessing a model's performance and its ability to generalise to new data [51]. K-fold cross-validation is a widely utilised technique in machine learning that involves partitioning the training dataset into K equal segments. In each iteration, one segment serves as the validation set, while the remaining segments collectively form the training set. This iterative process is repeated K times, ensuring that each segment is used as the validation set exactly once. Consequently, this method yields K distinct accuracy values, from which the overall performance of the model is assessed by calculating their average.