6th Edition of Neurology World Conference 2026

Abstract

Vaping affects over 100 million people worldwide and is the leading form of tobacco use among youth. Yet existing interventions remain reactive, non-individualized, and achieve only 24–50% short-term cessation success. The central scientific gap is temporal: no current framework identifies when a specific individual enters a window of heightened vaping vulnerability before craving onset. This paper presents PIVOT, grounded in Addiction Resonance Theory (ART), a novel framework modeling vaping vulnerability as resonance arising from three interacting systems: a Biological Layer of physiological oscillators, a Bee Layer of environmental trigger modulation, and an Octopus Layer of competing neural decision modules. PIVOT employs a three-phase physiologically constrained digital data amplification pipeline: a real survey of 973 adult vapers, calibrated stochastic expansion to 10,000 digitally amplified profiles (24M raw data points), and lossless Nyquist-Shannon compression to 8.65M training samples (63.9% volume reduction). Nine ML models are evaluated; the Neural Network achieves the highest accuracy (94.1%, AUC 0.988) while CatBoost is selected as the production model (93.4%, AUC 0.986) for inference speed, deployment simplicity, and exact SHAP interpretability via TreeExplainer. Tenfold cross-validation confirms 93.39% ± 0.15% accuracy. Layer-wise ablation validates ART: no single layer exceeds 74.6% accuracy; only their combination reaches 93.4%. SHAP analysis identifies stress, social proximity, and goal-setting as dominant predictors of vaping vulnerability, consistent with findings reported by Rahimi et al. across an independent cohort of 1,119 vapers.