## 4 Discussion
The results confirmed that higher levels of self-efficacy were significant predictors of successful cessation, highlighting the importance of subjective confidence in one's ability to quit over other demographic factors. The performed PCA shows that self-reported perceptions can be meaningfully distinguished in reduced-dimensional space. This supports the interpretive value of subjective assessments in modeling family-related outcomes, even when working with a relatively small sample. The satisfactory result of this reduction further validates the relevance of the selected features and justifies their use in subsequent classification analyses. These findings align with previous research emphasizing the significance of self-reported perceptions in understanding smoking behaviors and their impacts on family dynamics. Previous studies have demonstrated that adolescents' perceptions of smoking-related risks and benefits are closely associated with their smoking behaviors, which underscores the importance of subjective assessments in predicting smoking initiation and cessation [42]. Additionally, research has demonstrated that self-reported measures of nicotine dependence are effective predictors of smoking cessation outcomes, underscoring the value of subjective assessments in modeling smoking behaviors [43]. Overall, the results emphasize the need for targeted interventions addressing specific behavioral patterns, such as nocturnal smoking and early-morning cigarette use, as well as familial dynamics. The study's results provide actionable insights that can guide social interventions. For instance, the positive correlation between family tension and cigarette consumption highlights the need to integrate family counseling or stress-reduction strategies into smoking cessation programs. Social campaigns should not only target individual behavior but also promote healthy communication and shared activities within households, especially those with dual users. Additionally, machine learning models developed in this study could be further adapted to identify individuals at high risk of dependence and tailor prevention strategies accordingly. The strong predictive performance of the ensemble model suggests its potential application in future behavioral studies. Further research is needed to refine machine learning approaches for health behavior prediction and to explore the nuanced roles of socio-demographic factors in smoking cessation. ML models are a potent tool for studying the behavioral patterns linked to smoking and forecasting the results of quitting. These models can reveal complex patterns and correlations that conventional analytical techniques would overlook by using huge, varied datasets and advanced algorithms. The advancement of public health initiatives targeted at lowering smoking rates and enhancing the results for those trying to stop smoking depends on the ongoing development and use of machine learning techniques in this field [39]. This paper is not free of limitations. Although the findings are informative, the relatively small sample size of 100 participants presents a notable limitation. Small sample studies can yield useful insights, especially in exploratory research, but they limit the statistical power and generalizability of the results. In particular, subgroup-specific analyses--such as those based on gender or living environment--should be interpreted with caution. Future research should aim to recruit a larger and more diverse cohort to ensure greater external validity and robustness of the observed patterns. This relatively small sample size, though sufficient for preliminary insights, imposes limitations on the generalizability of the findings. A small sample reduces the statistical power of the analyses and increases the likelihood of sampling bias, where the observed results may not accurately represent the broader population.