Skip to main content
SpringerLink
Log in

Prediction of Liver Diseases Based on Machine Learning Technique for Big Data

  • Conference paper
  • First Online:
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

Liver diseases have produced a big data such as metabolomics analyses, electronic health records, and report including patient medical information, and disorders. However, these data must be analyzed and integrated if they are to produce models about physiological mechanisms of pathogenesis. We use machine learning based on classifier for big datasets in the fields of liver to Predict and therapeutic discovery. A dataset was developed with twenty three attributes that include the records of 7000 patients in which 5295 patients were male and rests were female. Support Vector Machine (SVM), Boosted C5.0, and Naive Bayes (NB), data mining techniques are used with the proposed model for the prediction of liver diseases. The performance of these classifier techniques are evaluated with accuracy, sensitivity, specificity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. http://www.who.int/countries/egy/en/

  2. Kumar, Y., Sahoo, G.: Prediction of different types of liver diseases using rule based classification model. Technol. Health Care 21(5), 417–432 (2013)

    Google Scholar 

  3. Roy, S., Singh, A., Shadev, S.K.: Machine learning method for classification of liver disorders. Far East J. Electron. Commun. 16(4), 789 (2016)

    Article  Google Scholar 

  4. Zhang, Y., et al.: A systems biology-based classifier for hepatocellular carcinoma diagnosis. PLoS One 6(7), e22426 (2011)

    Article  Google Scholar 

  5. Kavakiotis, I., et al.: Machine learning and data mining methods in diabetes research. Comput. Struct. Biotech. J. 15, 104–116 (2017)

    Article  Google Scholar 

  6. Ayodele, T.O.: Types of machine learning algorithms. In: New Advances in Machine Learning. InTech (2010)

    Google Scholar 

  7. Gullo, F.: From patterns in data to knowledge discovery: what data mining can do. Phys. Procedia 62, 18–22 (2015)

    Article  Google Scholar 

  8. Pandey, B., Singh, A.: Intelligent techniques and applications in liver disorders. Survey, January 2014

    Google Scholar 

  9. Takkar, S., Singh, A., Pandey, B.: Application of machine learning algorithms to a well defined clinical problem: liver disease. Int. J. E-Health Med. Commun. (IJEHMC) 8(4), 38–60 (2017)

    Article  Google Scholar 

  10. Siuly, S., Zhang, Y.: Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci. Eng. 1(2), 54–64 (2016)

    Article  Google Scholar 

  11. Luo, J., et al.: Big data application in biomedical research and health care: a literature review. Biomed. Inform. Insights 8, 1 (2016)

    Google Scholar 

  12. Ramana, B.V., Babu, M.S.P., Venkateswarlu, N.B.: A critical study of selected classification algorithms for liver disease diagnosis. Int. J. Database Manag. Syst. (IJDMS) 3(2), 1–14 (2011)

    Article  Google Scholar 

  13. Bujlow, T., Riaz, T., Pedersen, J.M.: A method for classification of network traffic based on C5.0 machine learning algorithm. In: 2012 International Conference on Computing, Networking and Communications (ICNC). IEEE (2012)

    Google Scholar 

  14. Ozcift, A., Gulten, A.: Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput. Methods Programs Biomed. 104(3), 443–451 (2011)

    Article  Google Scholar 

  15. Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9(01), 1 (2017)

    Google Scholar 

  16. Singh, A., Pandey, B.: Intelligent techniques and applications in liver disorders: a survey. Int. J. Biomed. Eng. Technol. 16(1), 27–70 (2014)

    Article  Google Scholar 

  17. http://www.scymed.com/en/smnxpc/pcdcc770.htm

Download references

Author information

Authors and Affiliations

  1. Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Engy A. El-Shafeiy & Ali I. El-Desouky

  2. Computers Engineering Department, MISR Higher institute for Engineering and Technology, Al Mansora, Egypt

    Sally M. Elghamrawy

Authors
  1. Engy A. El-Shafeiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali I. El-Desouky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sally M. Elghamrawy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Engy A. El-Shafeiy .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El-Shafeiy, E.A., El-Desouky, A.I., Elghamrawy, S.M. (2018). Prediction of Liver Diseases Based on Machine Learning Technique for Big Data. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74690-6_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation