Copyright
©The Author(s) 2018.
World J Hepatol. Dec 27, 2018; 10(12): 934-943
Published online Dec 27, 2018. doi: 10.4254/wjh.v10.i12.934
Published online Dec 27, 2018. doi: 10.4254/wjh.v10.i12.934
Table 1 The positive predictive value, negative predictive value, sensitivity, specificity and predictive accuracy rate of each category
| Positive predictive value | Negative predictive value | Sensitivity | Specificity | Predictive accuracy rate | |
| HAIR score | 92.6% | 20.0% | 38.5% | 83.3% | 45.5% |
| BARD score | 78.0% | 8.3% | 49.2% | 25.0% | 45.5% |
| JOMO algorithm | 88.9% | 35.7% | 86.2% | 41.7% | 79.2% |
- Citation: Uehara D, Hayashi Y, Seki Y, Kakizaki S, Horiguchi N, Tojima H, Yamazaki Y, Sato K, Yasuda K, Yamada M, Uraoka T, Kasama K. Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology. World J Hepatol 2018; 10(12): 934-943
- URL: https://www.wjgnet.com/1948-5182/full/v10/i12/934.htm
- DOI: https://dx.doi.org/10.4254/wjh.v10.i12.934