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Soft Computing

, Volume 23, Issue 21, pp 10931–10938 | Cite as

Intelligent hepatitis diagnosis using adaptive neuro-fuzzy inference system and information gain method

  • Waheed AhmadEmail author
  • Ayaz Ahmad
  • Amjad Iqbal
  • Muhammad Hamayun
  • Anwar Hussain
  • Gauhar Rehman
  • Salman Khan
  • Ubaid Ullah Khan
  • Dawar Khan
  • Lican HuangEmail author
Methodologies and Application
  • 95 Downloads

Abstract

Hepatitis, a common liver inflammation, is one of the major public health issues around the world. Proper interpretation of clinical data for the diagnosis of hepatitis is an important problem that needs to be addressed. In this study, a hybrid intelligent approach, combining information gain method and adaptive neuro-fuzzy inference system (ANFIS), is proposed for the diagnosis of fatal hepatitis disorder. Initially, the hepatitis dataset obtained from the University of California Irvine machine learning repository is preprocessed to make it suitable for the mining process. After the preprocessing stage, information gain method is applied to condense the number of features in order to decrease computation time and classification complexity. Selected features are then fed into the ANFIS classifier system. The performance of the proposed approach was evaluated using statistical methods, and the highest results for the classification accuracy, specificity, and sensitivity analysis of the proposed system reached were 95.24%, 91.7%, and 96.17%, respectively. The obtained results show that the proposed intelligent system has a good diagnosis performance and can be applied as a promising tool for the diagnosis of hepatitis.

Keywords

Information gain (IG) The adaptive neuro-fuzzy inference system (ANFIS) Hepatitis Diagnosis Machine learning 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, School of InformaticsZhejiang Sci-Tech UniversityHangzhouChina
  2. 2.Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina
  3. 3.Department of BiotechnologyAbdul Wali Khan University MardanMardanPakistan
  4. 4.Department of AgricultureAbdul Wali Khan University MardanMardanPakistan
  5. 5.Department of BotanyAbdul Wali Khan University MardanMardanPakistan
  6. 6.Department of ZoologyAbdul Wali Khan University MardanMardanPakistan
  7. 7.Department of Computer SciencesAbdul Wali Khan University MardanMardanPakistan
  8. 8.Department of Computer SciencesComsats Institute of Information Technology AbbottabadIslamabadPakistan
  9. 9.Institute of AutomationChinese Academy of ScienceBeijingChina

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