Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China
Background. Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases. Machine learning techniques were introduced to evaluate the optimal predictive clinical model of NAFLD. Methods. A cross-sectional study was performed with subjects who attended a health examinatio...
Main Authors: | Han Ma, Cheng-fu Xu, Zhe Shen, Chao-hui Yu, You-ming Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2018-01-01
|
Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2018/4304376 |
Similar Items
-
Sarcopenia is associated with the presence of nonalcoholic fatty liver disease in Zhejiang Province, China: a cross-sectional observational study
by: Yu-Ming Wang, et al.
Published: (2021-01-01) -
Association of adult weight gain and nonalcoholic fatty liver in a cross-sectional study in Wan Song Community, China
by: W.-J. Zhang, et al.
Published: (2014-02-01) -
Dietary patterns in Brazilian patients with nonalcoholic fatty liver disease: a cross-sectional study
by: Silvia Marinho Ferolla, et al.
Published: (2013-01-01) -
Gut Microbiota Dysbiosis in Patients with Biopsy-Proven Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study in Taiwan
by: Ming-Chao Tsai, et al.
Published: (2020-03-01) -
Comparison of obesity-related indices for identifying nonalcoholic fatty liver disease: a population-based cross-sectional study in China
by: Fangfei Xie, et al.
Published: (2021-10-01)