Enhanced Bayesian Network Integration for Early-Stage Lung Cancer Risk Prediction via Voice Biomarker Analysis

**Abstract:** This paper introduces a novel approach to early-stage lung cancer risk prediction leveraging a Bayesian Network (BN) framework integrated with voice biomarker analysis. Current AI-driven cancer risk assessment tools often rely heavily on structured clinical data, overlooking readily accessible and inexpensive physiological signals. By incorporating voice features indicative of subtle physiological changes associated with early-stage lung cancer, this system enhances predictive accuracy and offers a non-invasive, scalable screening tool. The system incorporates a multi-layered evaluation pipeline to ensure logical consistency, formulaic validity, novelty assessment, and impact forecasting, culminating in a HyperScore to quantify risk. This approach aims to improve early detection rates, reduce unnecessary biopsies, and ultimately improve patient outcomes. It is immediately commercially viable thanks to established processing techniques and readily attainable data acquisition methods.