6 引文 斯高帕斯(Scopus)

摘要

Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In this proposed model, logistic regression technique provides a better result (Accuracy 76.30%, sensitivity 74.10%, and specificity 64.90%) among all other techniques. This study demonstrates that machine learning models particularly logistic regression model provides a higher accurate prediction for fatty liver diseases based on medical data from electronic medical. This model can be used as a valuable tool for clinical decision making.
原文英語
主出版物標題Building Continents of Knowledge in Oceans of Data
主出版物子標題The Future of Co-Created eHealth - Proceedings of MIE 2018
發行者IOS Press
頁面166-170
頁數5
ISBN(電子)9781614998518
DOIs
出版狀態已發佈 - 一月 1 2018
事件40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, 瑞典
持續時間: 四月 24 2018四月 26 2018

出版系列

名字Studies in Health Technology and Informatics
247
ISSN(列印)0926-9630
ISSN(電子)1879-8365

其他

其他40th Medical Informatics in Europe Conference, MIE 2018
國家瑞典
城市Gothenburg
期間4/24/184/26/18

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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