A machine learning approach to screening of non–small cell lung cancer using metabolic data
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Date
2020-10
Authors
Trevena, Connel
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Abstract
The human metabolome represents a largely unexplored area with respect to prediction of disease. I have conducted a targeted study of metabolic compounds in the human metabolome that are linked to patients with non small cell lung cancer. Through the use of machine learning techniques such as SVMs, Random Forests, and Decision Trees, I have determined models can be trained to correctly classify new patients with an F1--score above 0.95 in case vs. control classification. From these models I have produced a select subset of compounds using peak analysis as well as recursive feature extraction such that when the prediction is done on this smaller subset of compounds an F1--score of above 0.95 can still be achieved. These compounds represent potential biomarkers for future studies and clinical applications.
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Keywords
Metabalomics, Machine Learning, Non small-cell lung cancer, Bioinformatics