Integrating Predictive Analytics and Explainable AI for Lung Cancer Detection within the One Health Framework
DOI:
https://doi.org/10.5281/zenodo.19053091Keywords:
Sustainable agriculture, Crop yield prediction, Machine learning (ML), Deep learning, Explainable artificial intelligence (XAI), LIME (Local Interpretable Model-Agnostic Explanations), SHAP (Shapley Additive explanations), Model interpretability, Data quality, Model generalizability.Abstract
Lung cancer is one of the biggest causes of cancer death worldwide, mainly because it often goes undiagnosed until it is too late for effective treatment. Most patients will not have signs or symptoms of lung cancer until well into the disease, when treatment is likely less effective. Once cancer is diagnosed, the chance of survival decreases significantly, and treatment becomes more aggressive, costly, and uncertain. Even with new imaging technologies and improved ways of diagnosing lung cancer, early detection of the disease is still extremely difficult because of the many different genetic, environmental, and lifestyle factors that contribute to lung cancer. For these reasons, we are taking a holistic One Health approach to this research. The focus of this project will be to study and analyze how the interrelationships between health, environmental exposure, and lifestyle choices (e.g., smoking, air pollution, occupational exposure, socioeconomic status) contribute to the development and progression of lung cancer. This study takes into account all of these various elements and moves away from lung cancer research that views lung cancer in only a clinical context to understand it in an ecological and systems-oriented perspective. The main objective of this research is to utilize predictive analytics and machine learning (ML) techniques to analyze health data to determine the factors associated with an increased risk for developing lung cancer. ML and predictive analytics are technologies that allow us to find hidden patterns and non-linear relationships in large, complex data sets. Several different ML algorithms will be used to predict lung cancer, including classification models like Logistic Regression, Random Forest, and Gradient Boosting Machines.
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