Advertisement

ANN Model for Liver Disorder Detection

  • Shubham DhingraEmail author
  • Ishaan Singh
  • R. Subburaj
  • Shalini Diwakar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 612)

Abstract

Liver plays a significant role and hence considered to be the major organ in human body. Diagnosis of any disorder in liver at initial stage is vital for its recovery. Liver diseases occur because of high rate of alcohol consumption, consumption of contaminated water or food and being exposed to toxic gases, etc. Early prediction of the disease can save a life. Evaluation of the classification algorithms: Logistic Regression, Support Vector Machine, Naive Bayes, and Artificial Neural Network are done in this paper by carrying out the experimental approach in Anaconda by using “sklearn” and “keras” libraries. The dataset has been acquired from UCI Machine Learning repository with 10 major attributes. The aim is to find the model which most accurately predicts any disorder in the liver. Through our experiment, Artificial Neural Network has the best fit with accuracy of 80.70%, making it better than other techniques.

Keywords

Machine learning Artificial neural network (ANN) Liver 

References

  1. 1.
    Sug H (2012) Improving the prediction accuracy of liver disorder disease with oversampling. Appl Math Electr Comput EngGoogle Scholar
  2. 2.
    Sathya R, Abrahim A (2013) Comparison of supervised and unsupervised learning algorithms for pattern classification. Int J Adv Res Artif IntellGoogle Scholar
  3. 3.
    Tiwari AK, Sharma LK, Ramakrishna G (2013) Comparative study of artificial neural network based classification of liver patient. J Inf Eng ApplGoogle Scholar
  4. 4.
    Ramanaland BV, Babu, MSP (2012) Liver classification using modified rotation forest. Int J Eng Res Dev 1(6) (2012) ISSN: 2278-067XGoogle Scholar
  5. 5.
    Selvara G, Janakiraman, S (2013) A study of textural analysis methods for the diagnosis of liver disease from abdominal computed tomography. Int J Comput Appl 74(11) ISSN: 0975-8887CrossRefGoogle Scholar
  6. 6.
    Sindhuja D, Priyadarsini RJ (2016) A survey on classification techniques in data mining for analyzing liver disease disorder. Int J Comput Sci Mob Comput 5(5)Google Scholar
  7. 7.
    Vijayarani, S, Dhayanand S (2015) Liver disease prediction using SVM and naïve bayes algorithms. Int J Sci Eng Technol ResGoogle Scholar
  8. 8.
    Huang, L-C, Hsu S-Y, Lin, E (2009) A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic dataGoogle Scholar
  9. 9.
    Ling-Min H, Yang X-B, Lu H-J (2007) A comparison of support vector machines ensemble for classification. In: Machine Learning and CyberneticsGoogle Scholar
  10. 10.
    Lin R-H (2009) An intelligent model for liver disease diagnosis. Artificial Intelligence in MedicineGoogle Scholar
  11. 11.
    Srivastava DK, Lekha B Data classification using support vector machine. J Theor Appl Inf TechnolGoogle Scholar
  12. 12.
    Prasad Babu MS, Ramana BV, Sarath Kumar BR, New automatic diagnosis of liver status using bayesian classificationGoogle Scholar
  13. 13.
    Tapas Ranjan B, Pani SK (2016) Analysis of data mining techniques for healthcare decision support system using liver disorder dataset. Procedia Comput. Sci. 85Google Scholar
  14. 14.
    Schiff ER, Sorrell, MF, Maddrey WC Schiff’s diseases of the liver, 10th edition Lippincott Williams & WilkinsGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shubham Dhingra
    • 1
    Email author
  • Ishaan Singh
    • 1
  • R. Subburaj
    • 1
  • Shalini Diwakar
    • 1
  1. 1.SRM Institute of Science and TechnologyChennaiIndia

Personalised recommendations