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...
| Publicado en: | BioMed Research International pp. 1 - 10 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/3/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132116216&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132116216 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/3/2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 132116216 132116216 132116216 10.1155/2018/4304376 132116216 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China. aug: au: Ma, Han Xu, Cheng-fu Shen, Zhe Yu, Chao-hui Li, You-ming affil: Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang Province, China sug: subj: Nonalcoholic Fatty Liver Disease Diagnosis Machine Learning Utilization Human China Cross Sectional Studies Academic Medical Centers Questionnaires Diagnosis, Laboratory Physical Examination Ultrasonography Methods Liver Ultrasonography Software Body Mass Index Triglycerides Blood gamma-Glutamyltransferase Blood Alanine Aminotransferase Blood Uric Acid Blood Probability Sensitivity and Specificity Logistic Regression Predictive Value of Tests ab: 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 examination at the First Affiliated Hospital, Zhejiang University. Questionnaires, laboratory tests, physical examinations, and liver ultrasonography were employed. Machine learning techniques were then implemented using the open source software Weka. The tasks included feature selection and classification. Feature selection techniques built a screening model by removing the redundant features. Classification was used to build a prediction model, which was evaluated by the F-measure. 11 state-of-the-art machine learning techniques were investigated. Results. Among the 10,508 enrolled subjects, 2,522 (24%) met the diagnostic criteria of NAFLD. By leveraging a set of statistical testing techniques, BMI, triglycerides, gamma-glutamyl transpeptidase (γGT), the serum alanine aminotransferase (ALT), and uric acid were the top 5 features contributing to NAFLD. A 10-fold cross-validation was used in the classification. According to the results, the Bayesian network model demonstrated the best performance from among the 11 different techniques. It achieved accuracy, specificity, sensitivity, and F-measure scores of up to 83%, 0.878, 0.675, and 0.655, respectively. Compared with logistic regression, the Bayesian network model improves the F-measure score by 9.17%. Conclusion. Novel machine learning techniques may have screening and predictive value for NAFLD. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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