Bayesian Additive Regression Trees for Predicting Colon Cancer: Methodological Study (Validity Study).
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Date
2022-05-01
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Turkiye klinikleri
Abstract
The occurrence of colon cancer starts in the inner wall of the large intestine. The survival of colon cancer patients strongly relies on early detection. Diagnosing colon cancer using clinical approaches often takes longer, especially in most developing countries with limited facilities. The recent use of microarray technology has presented a new approach for the oncologist to diagnose cancer cells using non-clinical machine learning methods. In this paper, the aim is to predict the status of colon cancer tissues using the Bayesian Additive Regression Trees (BART) and 2 other machine learning methods. Material and Methods: The development and comparative analysis of BART alongside 2 other competing methods (Random Forest: RF and Gradient Boosting Machine: GBM) were implemented. The dataset used for the analysis is the microarray colon cancer data which consists of 2,000 gene expression …
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Olaniran O.R., Olaniran S.F., J. Popoola, I.V Omekam (2022). Bayesian Addictive Regression Trees for Predicting Colon Cancer. Turkiye Klinikleri Journal of Biostatistics 2022;14(2) doi:10.5336/biostatic.2022-89894 https://search.trdizin.gov.tr/tr/yayin/detay/1125578/bayesian-additive-regression-trees-for-predicting-coloncancer- methodological-study-validity-study