From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health
DOI:
https://doi.org/10.56294/mw2024.670Keywords:
Cardiovascular Health, Wearable Technology, Biomolecular Signatures, Smoking, Machine Learning, Proteomics, Epigenetics, Preventive Healthcare, Mendelian RandomizationAbstract
Tobacco smoking keeps to exert a profound effect on cardiovascular health, contributing to situations including arterial stiffness, hypertension, and microcirculatory disorder. Traditional studies strategies, often siloed into remoted domains like biomarker analysis or behavioral surveys, fail to seize the dynamic interplay between smoking behaviors and biological disruptions. This take a look at integrates AI-driven analytics, wearable sensor networks, and deep biomolecular profiling to map smoking’s multidimensional effects. By combining actual-time physiological statistics (e.g., PPG, HRV) with epigenetic and proteomic markers, the research objectives to are expecting individual cardiovascular risks and enable preemptive interventions. Results reveal the efficacy of ensemble models like Random Forest (AUC = zero.889) in taking pictures complex interactions among variables consisting of γ-GTP, waist circumference, and blood stress. The paintings highlight the capability of AI and wearables to convert reactive healthcare into personalized, preventive strategies.
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