Optimizing Smoking Cessation Alternatives Using Multi-MOORA and AI-Based Methods

Joanna Chwał, Arkadiusz Banasik, Radosław Dzik, Ewaryst Tkacz

Abstract: This study aims at enhancing smoking cessation strategies by combining Multi-Objective Optimization on Ratio Analysis (Multi-MOORA) with artificial intelligence (AI) approaches. The hybrid model including neural networks, ridge regression, and simulated annealing compares various smoking cessation options based on cost, age of initiation, time spent smoking, ease of quitting, environmental and health impacts. The model’s stability and accuracy were tested using a survey and secondary data, and the findings show that e-cigarettes are the most favorable alternative. The sensitivity analysis also validates the robustness of the ranking process. The approach provides a decision-making framework that can help in optimizing smoking cessation interventions and can help in developing personalized and evidence-based public health policy.

External IDs:doi:10.1007/978-3-032-13056-3_30