Futuristic Blockchain Based Computer Vision Technique for Environmentally Informed Smoking Cessation: A Revolutionary Approach to Predictive Modeling

The global health crisis induced by smoking and its detrimental impact on the environment necessitates an innovative and robust approach to promoting smoking cessation. This research addresses these exigencies by introducing a groundbreaking methodology that converges advanced technologies, including computer vision, machine learning, and blockchain. By visually communicating the environmental toll of smoking, we capture individuals' emotional reactions using computer vision-based facial emotion detection. This valuable information, securely and indelibly recorded on a blockchain network, upholds data integrity, and ensures privacy. The amalgamation of these responses forms the crux of our study, serving as the input for a series of machine learning classifiers, namely Decision Tree, Gradient Boosting, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, and XGBoost. The analysis using these classifiers identifies visual and emotional triggers that might influence an individual's decision to quit smoking. The performance of the classifiers was highly promising, with Random Forest, Gradient Boosting, and SVM demonstrating an impressive accuracy of 98%. This pioneering research provides not only a fresh perspective on personalized, effective smoking cessation strategies but also showcases the potential of blockchain as a reliable data management tool in health research.