# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 4 Results and Discussion
Typically, the values of K most frequently adopted are 5 and 10, as they strike a balance between computational efficiency and the reliability of performance estimates [52]. As the dataset grows larger, it is advisable to increase the K value. For this study, a K value of 10 was selected. The study results were evaluated, analysed, and interpreted using several methods, including the coefficient of determination (R\({}^{2}\)), mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The R\({}^{2}\) serves as an indicator of the concordance between the actual values and the predictions generated by a model. It is computed by taking the ratio of the variance explained by the predicted values to the total variance present in the actual values. This statistical measure effectively conveys the effectiveness of the model's fit, providing insights into its predictive accuracy. The R-squared metric takes a value between 0% and 1% after the mathematical process. Here, 0% indicates that the model explains nothing, and the model fit is not good [53]. This outcome suggests that the current data are not appropriate for the model, necessitating a change in the model. On the other hand, a score of 1% signifies that the model offers a perfect explanation of the data, indicating an excellent fit [54]. The MSE serves as an evaluative statistical metric that quantifies the performance of predictive models by calculating the average of the squares of the deviations between the observed actual values and their corresponding predicted counterparts. This metric is instrumental in determining the predictive accuracy of a model. Conversely, the MAE assesses model accuracy by averaging the absolute discrepancies between the actual and predicted values. This metric prioritises the actual magnitude of the prediction errors, thereby providing direct insights into the model's performance. Furthermore, the MAPE offers a comparative measure of forecast inaccuracy by expressing it in percentage terms relative to the actual demand. This renders the MAPE particularly intuitive and user-friendly for interpretive purposes. Importantly, the MAPE metric circumvents the challenges associated with comparing models evaluated across different units of measurement, offering a standardised comparative framework [55]. Among these performance measures, the R\({}^{2}\) serves as the coefficient of determination to assess the model's accuracy. A high value for this coefficient indicates a good prediction relationship. The MSE, MAE, and MAPE, on the other hand, are error measures, and low results indicate high performance, being inversely proportional to the performance [56; 57]. In this study, four distinct machine learning methodologies were employed: an ANN, RF regression, SVR, and time series. For the ANN approach, a feedback model was designed, comprising eleven input neurons and a single output neuron. Through iterative experimentation, it became evident that a configuration featuring four hidden layers, each containing four neurons, yielded more effective outcomes. The architecture of the model developed is depicted in Figure 2.  
Following an extensive evaluation of several potential activation functions, the sigmoid function emerged as the most effective option due to its superior performance. The sigmoid activation function is characterised by its continuous and differentiable nature, yielding output values constrained between 0 and 1 for any input. In the context of the learning method, a feedback learning algorithm was employed, serving as a critical parameter influencing the model's success in ANNs. In a feedback ANN, at least one cell generates an output that is subsequently fed back as an input either to itself or other cells. This feedback mechanism may occur among cells within the same layer or across different layers [58]. In the model, 500 iterations were performed to determine the best result. The Gini index is preferred as the division criterion in the RF method.