Evolutionary ensemble learning algorithm to modeling of warfarin dose prediction for Chinese

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@Article{Tao:2018:ieeeJBHI,

• author = "Yanyun Tao and Yenming J. Chen and Xiangyu Fu and Bin Jiang and Yuzhen Zhang",
• journal = "IEEE Journal of Biomedical and Health Informatics",
• title = "Evolutionary ensemble learning algorithm to modeling of warfarin dose prediction for Chinese",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, warfarin dose prediction, ensemble modeling, machine learning, regression model, genetic programming",
• ISSN = "2168-2194",
• DOI = "doi:10.1109/JBHI.2018.2812165",
• size = "12 pages",
• abstract = "An evolutionary ensemble modelling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and genetic algorithm optimises the parameters of the GP. The EEM model is assembled by using the prepared based models through a technique called bagging. In the experiment, a dataset of 289 Chinese patients, which is provided by The First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with M2+G group, namely, age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R2), highest percentage of predicted dose within 20percent of the actual dose (20percent-p), smallest mean absolute error (mae), mean squared error (mse), root-mse on the test set, and the least decrease in R2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can therefore be a suitable prediction model of warfarin dose for clinical application.",
• notes = "Soochow University, Suzhou, China. National Kaohsiung University of Science & Technology, Taiwan, R.O.C. The First Affiliated Hospital of Soochow University, Suzhou, China, Also known as \cite{8306883}",

}

Genetic Programming entries for Yanyun Tao Yenming J Chen Xiangyu Fu Bin Jiang Yuzhen Zhang

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