TY - GEN
T1 - Applications of machine learning in fatty live disease prediction
AU - Mohaimenul Islam, Md
AU - Wu, Chieh Chen
AU - Poly, Tahmina Nasrin
AU - Yang, Hsuan Chia
AU - Li, Yu Chuan
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85046535293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046535293&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-852-5-166
DO - 10.3233/978-1-61499-852-5-166
M3 - Conference contribution
C2 - 29677944
AN - SCOPUS:85046535293
T3 - Studies in Health Technology and Informatics
SP - 166
EP - 170
BT - Building Continents of Knowledge in Oceans of Data
PB - IOS Press
T2 - 40th Medical Informatics in Europe Conference, MIE 2018
Y2 - 24 April 2018 through 26 April 2018
ER -