Artificial Intelligence in Smoking Residue Detection: Bridging the Gap Between Medical Diagnostics and Predictive Analysis

The objective of this work was to create a model that could identify smoking traces in the body and forecast future smoking propensity using a variety of healthrelated variables. Effective detection and monitoring of smoking residues in people is essential for identifying smoking behaviors and evaluating health concerns. The researchers used a cutting-edge strategy that combined medical diagnostics with artificial intelligence (AI) to enable advanced detection of smoking residues in order to overcome this barrier. The suggested methodology makes use of medical diagnostic tools, including an individual's lipid profile and dental test, to record and examine physiological and chemical indications connected to smoking. The vast data generated by modern medical diagnostic methods are meticulously analyzed and comprehended by AI-based systems to get improved accuracy and effectiveness of detecting smoking residue. Voluminous data sets serve as a crucial training ground for machine learning models, enabling them to discern patterns and accurately classify individuals based on their smoking habits. The study demonstrated a 99% prediction performance, making it a valuable tool for healthcare institutions to better understand and predict the likelihood of hospital admissions related to smoking. In the future, the study aims to determine the concentration of nicotine or cotinine and detect heart disease and lung conditions.