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.

Original languageEnglish
Title of host publicationBuilding Continents of Knowledge in Oceans of Data
Subtitle of host publicationThe Future of Co-Created eHealth - Proceedings of MIE 2018
EditorsAdrien Ugon, Daniel Karlsson, Gunnar O. Klein, Anne Moen
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781614998518
Publication statusPublished - 2018
Event40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden
Duration: Apr 24 2018Apr 26 2018

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other40th Medical Informatics in Europe Conference, MIE 2018

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


Dive into the research topics of 'Applications of machine learning in fatty live disease prediction'. Together they form a unique fingerprint.

Cite this