## 4 Discussion  
Smoking significantly increases the risk of developing respiratory diseases, cardiovascular problems, and various forms of cancer, severely impacting overall health and longevity [32]. The findings of this study provide valuable insights into the socio-demographic, familial, and behavioral factors influencing smoking behaviors and cessation attempts. The lack of significant association between strong urges to smoke and cessation attempts (\(\chi^{2}\) = 5.1532, \(p\) = 0.27194) challenges prior studies, such as [PERSON] et al. (2011) [33], which emphasized craving intensity as a predictor of cessation efforts. This suggests that a more comprehensive understanding of cessation predictors is necessary. Nocturnal smoking behavior was significantly correlated with higher cigarette consumption (\(\chi^{2}\) = 14.2456, \(p\) = 0.014122), reinforcing findings by [PERSON] et al. (2015) that such behavior reflects deeper addiction patterns [26]. The strong correlation between e-cigarette use and traditional smoking (\(\rho\) = 0.67921, \(p\) = 8.0027 \(\times\) 10\({}^{-15}\)) highlights the behavioral overlap and concurrent use of these products, as supported by [PERSON] and [PERSON] (2024) [32]. Age also played a critical role, with older participants initiating smoking at later ages (\(\rho\) = 0.22546, \(p\) = 0.0241), underscoring age-related differences in smoking initiation and patterns. Gender differences in smoking type (\(\chi^{2}\) = 10.63, \(p\) = 0.031) further emphasize the need to consider gender-specific interventions. The negative correlation between the time to the first cigarette after waking and willingness to quit (\(\rho\) = \(-\)0.30509, \(p\) = 0.002) suggests that earlier smoking upon waking may signify more entrenched addiction and lower cessation intent. Additionally, family dynamics significantly influenced smoking behaviors, with familial tension positively correlated with cigarette consumption (\(\rho\) = 0.22 to 0.34). This finding aligns with [PERSON] et al. (2020), who highlighted the impact of family conflict on smoking [34]. Machine learning models provided additional insights. The ensemble model's superior performance, with a cross-validation accuracy of 93.33%, demonstrates its robustness in identifying complex relationships within the dataset. This aligns with [PERSON] (2000), who emphasized the effectiveness of ensemble methods in handling intricate data structures [35]. The decision tree classifier demonstrated moderate predictive power, with a cross-validation accuracy of 83.33% and a loss of 18.57%, reflecting its ability to identify key decision nodes but with limitations in handling intricate patterns. The k-nearest neighbor (k-NN) model achieved a cross-validation accuracy of 90.00% and a loss of 14.29%, suggesting high sensitivity to localized data structures. Similar studies in behavioral prediction have also noted such trade-offs ([PERSON], 2024) [36]. The SVM model's lower performance (80.00% accuracy) reflects its challenges with mixed-variable datasets, consistent with findings by [PERSON] et al. (2024) [37]. Figure 10: Confusion matrices of ML models in sequence: decision tree confusion matrix, SVM confusion matrix, ensemble model confusion matrix, and k-NN confusion matrix. Color intensity of each cell represents the relative frequency of predictions. Darker shades of blue indicate a higher number of correctly classified instances, typically seen along the diagonal. Conversely, light blue to white indicates fewer correct predictions, while light red hues highlight misclassifications, positioned off the diagonal. The feature importance analysis across all four models (ensemble, SVM, decision tree, and k-NN) revealed a consistent pattern, highlighting the dominant role of the variable _NumericImpactResponses_. This feature was the most influential in predicting perceived impact regardless of the algorithm used, suggesting a strong and stable relationship between participants' responses regarding personal impact and the classification outcomes.