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©The Author(s) 2022.
World J Gastroenterol. Nov 28, 2022; 28(44): 6230-6248
Published online Nov 28, 2022. doi: 10.3748/wjg.v28.i44.6230
Published online Nov 28, 2022. doi: 10.3748/wjg.v28.i44.6230
Table 2 Summary of the most repeated inputs of the machine learning models with the most repeated predictor outcomes for the four main inflammatory-related liver conditions
Inflammatory-related liver condition | Inputs | Most repeated predictors |
FLD | Age, sex, blood biomarkers, and demographic, anthropometric, and clinical data | BMI, uric acid, TG, and ALT levels |
Liver fibrosis | Age, sex, and CT images | Better diagnosis compared to classical methods like APRI and FIB-4 indexes |
Virus-induced hepatitis | Age, sex, blood biomarkers, and demographic, anthropometric, and clinical data | AST, PLT levels, APRI index, and age |
COVID-19 | Age, sex, blood biomarkers, CT images, and demographic, anthropometric, and clinical data | Age, BMI, CT images, oxygen rate, AST, and ALT levels |
- Citation: Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28(44): 6230-6248
- URL: https://www.wjgnet.com/1007-9327/full/v28/i44/6230.htm
- DOI: https://dx.doi.org/10.3748/wjg.v28.i44.6230