Smoking Risk Prediction in Bangladesh Using Machine Learning

Smoking is a severe threat to public health that impacts Bangladesh's general population as well as university students. Many people engage up smoking as a habit as a result of personal stressors or social trends, frequently disregarding the adverse consequences of smoking. Thirty-five percent of Bangladeshi adults smoke or use electronic cigarettes, according to the WHO. This study investigates the root causes of smoking and its broader implications through a sustainable AI-driven approach. This is because our study to find out the reason behind it and the consequence of smoking risk in personal life. In this study, various aspects of life were considered to establish the effects of smoking. Most people who used to do daily smoke of what with a huge amount did not lead a better life in personal life. The use of these findings assists people, corporations, and authorities in identifying the primary hazards of smoking that may be lethal. To predict the Risk of smoking KNN, Logistic Regression, Random Forest, XGBoost, LightGBM, Decision Tree, Naive Bayes and SVM were practiced to get the model. By collecting data via the survey form and keeping nine questions from the service, 501 data collections were made from various persons. Among the models, SVM and XGBoost perform better than all other working classifiers attaining 98.40% and 97% accuracy. In this study, the integration of sustainable AI practice enhances resource scalability and long-term planning. It enables the allocation of resources better to achieve the desired outcomes besides other institution's health services to introduce and implement successful interventions. Specifically, this work underscores the significance of sustainable AI for development for improving various public health causes and forming a healthier and more equitable society.