Abstract
Lung cancer continues to be one of the leading causes of cancer-related mortality globally. While environmental factors, particularly smoking, are recognized as primary contributors, growing research shows that genetic predisposition, especially family-linked inheritance, plays a significant role in lung cancer susceptibility. Recent advancements in genomics and machine learning (ML) are providing new avenues for understanding these inherited risks. This review provides an in-depth analysis of how familial DNA influences lung cancer development, highlighting how machine learning models can be used to identify genetic markers associated with increased risk. By reviewing current findings and methodologies, we aim to bridge the gap between genetic susceptibility and the application of ML models in predictive healthcare.
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