Stacked Ensemble Model for Hepatitis in Healthcare System

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2019 by IJCOT Journal
Volume - 9 Issue - 4
Year of Publication : 2019
AuthorsAkinbohun Folake, Akinbohun Ambrose (Dr), Oyinloye Oghenerukevwe E. (Dr)
  10.14445/22492593/IJCOT-V9I4P305

MLA

MLA Style: Akinbohun Folake, Akinbohun Ambrose (Dr), Oyinloye Oghenerukevwe E. (Dr) "Stacked Ensemble Model for Hepatitis in Healthcare System" International Journal of Engineering Trends and Technology 9.4 (2019): 25-29.

APA Style: Akinbohun Folake, Akinbohun Ambrose (Dr), Oyinloye Oghenerukevwe E. (Dr) (2019). Stacked Ensemble Model for Hepatitis in Healthcare System International Journal of Engineering Trends and Technology, 9(4), 25-29.

Abstract

Hepatitis is an inflammatory condition of the liver caused by a viral infection. Viral hepatitis is of various types namely A, B, C, D and E. Data and analytics driven models can be applied in medical domain with the aid of machine learning to predict diseases. The increase of the epidemiology of hepatitis needs computational intelligent tool for prediction. The objective of this paper is to develop a stacked ensemble model for hepatitis. The paper considers two feature selection methods namely correlation and consistency methods on the whole hepatitis dataset obtained from UCI repository. The Stacking ensemble method was selected which combined multiple classifications namely Decision tree (C4.5) and Naive Bayes (the base-level classifiers) via a meta-classifier namely classification via regression where cross validation was applied. On the level of ensemble learning, when classification via regression was used at meta-level on the reduced dataset, the result indicated that correlation method in a stacked ensemble model produces better prediction for hepatitis than consistency method. Correlation method on Decision Tree model can be used for prediction of hepatitis

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Keywords
Consistency, decision tree, hepatitis, correlation