Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

doi:10.3748/wjg.v28.i46.6551

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Clinical and Translational Research

World J Gastroenterol. Dec 14, 2022; 28(46): 6551-6563
Published online Dec 14, 2022. doi: 10.3748/wjg.v28.i46.6551

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Figure 4 Ensemble accuracy. It visually depicts ensemble accuracy that is improved gradually with each iteration. The ensemble approach increases the accuracy level of the decision tree models.

Citation: Dalal S, Onyema EM, Malik A. Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World J Gastroenterol 2022; 28(46): 6551-6563
URL: https://www.wjgnet.com/1007-9327/full/v28/i46/6551.htm
DOI: https://dx.doi.org/10.3748/wjg.v28.i46.6551

Dalal S, Onyema EM, Malik A. Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World J Gastroenterol 2022; 28(46): 6551-6563 [PMID: 36569269 DOI: 10.3748/wjg.v28.i46.6551]

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