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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Dec 14, 2022 (publication date) through Jul 7, 2024

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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

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

Table 2 Result analysis

No.	Algorithm	Accuracy	AUC	Gini
1	CHAID model	71.36	0.746	0.493
2	CART model	73.24	0.724	0.448
3	Proposed model	93.55	0.987	0.974

AUC: Area under the curve; CART: Classification and regression tree; CHAID: Chi-square automated interaction detection.

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