Applications of machine learning in fatty live disease prediction

Md Mohaimenul Islam, Chieh Chen Wu, Tahmina Nasrin Poly, Hsuan Chia Yang, Yu Chuan Li

研究成果: 書貢獻/報告類型會議貢獻

2 引文 (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

指紋

Liver
Learning systems
Liver Diseases
Fatty Liver
Logistic Models
Logistics
Medical Electronics
Sensitivity and Specificity
Data Mining
Electronic medical equipment
Chronic Disease
Learning algorithms
Support vector machines
Data mining
Machine Learning
Decision making
Health
Neural networks
Forests
Therapeutics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

引用此文

Mohaimenul Islam, M., Wu, C. C., Poly, T. N., Yang, H. C., & Li, Y. C. (2018). Applications of machine learning in fatty live disease prediction. 於 Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018 (頁 166-170). (Studies in Health Technology and Informatics; 卷 247). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-166

Applications of machine learning in fatty live disease prediction. / Mohaimenul Islam, Md; Wu, Chieh Chen; Poly, Tahmina Nasrin; Yang, Hsuan Chia; Li, Yu Chuan.

Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. IOS Press, 2018. p. 166-170 (Studies in Health Technology and Informatics; 卷 247).

研究成果: 書貢獻/報告類型會議貢獻

Mohaimenul Islam, M, Wu, CC, Poly, TN, Yang, HC & Li, YC 2018, Applications of machine learning in fatty live disease prediction. 於 Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. Studies in Health Technology and Informatics, 卷 247, IOS Press, 頁 166-170, 40th Medical Informatics in Europe Conference, MIE 2018, Gothenburg, 瑞典, 4/24/18. https://doi.org/10.3233/978-1-61499-852-5-166
Mohaimenul Islam M, Wu CC, Poly TN, Yang HC, Li YC. Applications of machine learning in fatty live disease prediction. 於 Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. IOS Press. 2018. p. 166-170. (Studies in Health Technology and Informatics). https://doi.org/10.3233/978-1-61499-852-5-166
Mohaimenul Islam, Md ; Wu, Chieh Chen ; Poly, Tahmina Nasrin ; Yang, Hsuan Chia ; Li, Yu Chuan. / Applications of machine learning in fatty live disease prediction. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. IOS Press, 2018. 頁 166-170 (Studies in Health Technology and Informatics).
@inproceedings{fc249f58a373420f96e72a34e10674c8,
title = "Applications of machine learning in fatty live disease prediction",
abstract = "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.",
author = "{Mohaimenul Islam}, Md and Wu, {Chieh Chen} and Poly, {Tahmina Nasrin} and Yang, {Hsuan Chia} and Li, {Yu Chuan}",
year = "2018",
month = "1",
day = "1",
doi = "10.3233/978-1-61499-852-5-166",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "166--170",
booktitle = "Building Continents of Knowledge in Oceans of Data",
address = "United States",

}

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

ER -