Copyright
©The Author(s) 2022.
World J Gastroenterol. Dec 14, 2022; 28(46): 6551-6563
Published online Dec 14, 2022. doi: 10.3748/wjg.v28.i46.6551
Published online Dec 14, 2022. doi: 10.3748/wjg.v28.i46.6551
Table 1 Dataset description
No. | Attribute | Information | Type |
1 | Age | How old is the patient? | Integer |
2 | Sex | Patient’s sex | String (male/female) |
3 | Tot_bilirbn | Level of bilirubin compound | Floating |
4 | Direct_bilirbn | Level of direct bilirubin compound | Floating |
5 | Alk_phos | Level of alkaline phosphatase compound | Floating |
6 | Al_amntrfrs | Level of alanine aminotransferase compound | Floating |
7 | As_amntrfrs | Level of aspartate aminotransferase compound | Floating |
8 | Total_prot | Level of total protein in the sample | Floating |
9 | Albmn | Level of albumin in the sample | Floating |
10 | Ag_ratio | Ratio of compound albumin to globulin | Floating |
11 | Is_patient | Categorizes the dataset into parts | Categorical |
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 |
- 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