## 4 Discussion
In contrast, demographic variables such as gender, age, and smoking status contributed minimally across models, indicating that these factors were not key discriminators in this context. The ensemble and k-NN models concentrated importance almost exclusively on the _NumericImpactResponses_, while the SVM and decision tree models distributed minor importance to features such as _NumericFamilySmoking_ and _NumericConflictResponses_, pointing to potential secondary influences. These findings emphasize the value of subjective self-assessment measures in predictive modeling over more traditional demographic or behavioral variables and support the robustness of _NumericImpactRespons_ as a primary indicator across various algorithmic approaches. Machine learning (ML) models have demonstrated substantial utility in predictive analysis based on smoking behavioral patterns, notably in the area of smoking cessation. The integration of ML models in public health strategies provides a sophisticated approach to understanding and intervening in smoking behaviors effectively. Particularly in the field of smoking cessation, machine learning (ML) models have shown significant value in predicting analysis based on smoking behavioral patterns. A sophisticated method for comprehending and successfully addressing smoking behaviors is offered by the incorporation of machine learning algorithms into public health initiatives. Recent studies have successfully used machine learning (ML) models to predict smokers' cessation results and identified important factors that influence quitting. To predict smoking cessation among US citizens, for example, a study that used data from the Population Assessment of Tobacco and Health (PATH) used a variety of binary classifiers. This study demonstrated the ability of ML models to detect intricate associations within longitudinal data by identifying both known and new factors impacting the shift from current to former cigarette consumption [38]. Furthermore, applications that doctors and patients might use directly have been incorporated into the construction of prediction models for quitting smoking. These models provide tailored predictions that may dynamically respond to new data, modifying the expected success rates for quitting smoking in response to shifting input values [39]. This flexibility is essential in therapeutic settings because the unique qualities of each patient can have a big impact on how well they respond to treatment. In this area, machine learning is being used to solve problems like class imbalance in datasets, where the proportion of people quitting smoking is much lower than that of those who do not. To improve the predicted accuracy and dependability of ML models, sophisticated strategies such as ensemble methods and random oversampling and undersampling have been used [38]. By balancing the dataset, these techniques make sure that the majority class is not favored by the predictive models. Additionally, researchers can identify the elements that are most predictive of quitting smoking thanks to the variable importance and selection process in machine learning models. In order to guarantee that the models are trained on relevant, high-quality data, this procedure entails a great deal of data preparation and cleaning. The development of focused smoking cessation programs requires the identification of important predictors, such as nicotine dependency and the use of additional tobacco products [38; 39]. In our work, we propose predictive models that can be relatively simply implemented into social smoking cessation programs based on patient interviews, which take into account factors studied by questionnaire, as in our case. The findings of [PERSON] et al. (2017) [40] demonstrated that subjective measures of intention can be robust predictors of cessation behavior. [PERSON] et al. (2023) [41] studied the role of self-efficacy, a subjective self-assessment measure, in predicting smoking cessation among smokers attending a cessation program.