## 5 Conclusions  
This study provides critical insights into the complex interplay between smoking behaviors, familial dynamics, and addiction patterns, emphasizing the importance of addressing these factors in public health interventions. Key findings reveal that gender differences, family tension, and the timing of smoking after waking are pivotal factors influencing smoking habits and cessation efforts. Gender-specific smoking product choices and the association between higher family tension and increased smoking frequency highlight the need for personalized strategies to support individuals in high-stress familial environments. The correlation between smoking soon after waking and reduced willingness to quit underscores the importance of early behavioral patterns in shaping cessation outcomes. The analysis of familial dynamics revealed distinct differences among smoking groups. E-cigarette users reported stronger family support, higher levels of shared family time, and fewer conflicts compared to traditional cigarette smokers and dual users. However, they exhibited high nicotine dependence, with no participants in the e-cigarette or dual-use groups reporting an absence of dependence. Traditional cigarette smokers faced weaker familial bonds, marked by higher conflict levels and lower support. Dual users experienced the most challenging family dynamics, including the least shared time with family, coupled with the highest levels of nicotine dependence. These findings highlight the need for tailored interventions that simultaneously address familial challenges and addiction levels, particularly for dual users, who face compounded behavioral and relational difficulties. Machine learning techniques, particularly ensemble models, demonstrated significant potential in analyzing the relationships between smoking behaviors and associated factors. The ensemble model outperformed traditional methods, such as decision trees and support vector machines (SVMs), achieving superior accuracy and effectively identifying complex patterns within the dataset [37]. However, the study's relatively small sample size of 100 participants limits the generalizability of these findings. Small datasets reduce statistical power, increase sampling bias, and constrain the ability to identify subgroup-specific trends, such as those based on age, gender, or familial characteristics. Future research should address these limitations by increasing the sample size to enhance statistical robustness and generalizability. Expanding the diversity of the sample population and incorporating additional variables, such as socioeconomic status, stress,coping mechanisms, and mental health factors, would provide a more comprehensive understanding of smoking behaviors. Furthermore, testing machine learning models on external datasets from diverse populations will validate their reliability in broader contexts. Advanced feature engineering and improvements in ensemble techniques could further refine model performance and uncover additional nuanced relationships. In conclusion, this study highlights the profound influence of familial dynamics, addiction patterns, and demographic factors on smoking behaviors. It underscores the value of machine learning as a tool for behavioral research, offering a powerful means of analyzing complex datasets and informing targeted interventions. Public health strategies should consider the unique needs of different smoking groups. For example, interventions aimed at reducing family conflicts and promoting shared activities may benefit dual users, while cessation programs targeting e-cigarette and dual users should focus on mitigating high nicotine dependence. By addressing these multifaceted challenges, future efforts can more effectively reduce smoking prevalence and improve outcomes for individuals and their families. J.C.: Writing--original draft, Writing--review and editing, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. M.K.