IJPTT

Stacked Ensemble Model for Hepatitis in Healthcare System

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

Citation

MLA Style:Akinbohun Folake, Akinbohun Ambrose (Dr), Oyinloye Oghenerukevwe E. (Dr) "Stacked Ensemble Model for Hepatitis in Healthcare System" International Journal of Computer and Organization Trends 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 Computer and Organization Trends, 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

References

[1] Ahmed Mohamed Samir Ali Gamal Eldin (2011). A data mining approach for the prediction of hepatitis c virus protease cleavage sites. International Journal of Advanced Computer Science and Applications (IJACSA),vol. 2, No. 12. Page 179-182
[2] Fayyad U. M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. (1996). Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press
[3] Guyon I. and Elisseeff A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, vol. 3, pp. 1157-1182
[4] Han J, Kamber M, Pei J (2012). Data mining: concepts and techniques, 3rd edition. Elsevier, Amsterdam
[5] Jennifer H. MacLachin and Benjamin C. Cowie (2015). Hepatitis B Virus Epidemiology. Cold spring harb perspect med
[6] Nancy P., Sudha V. and Akiladevi R. (2017). Analysis of feature Selection and Classification algorithms on Hepatitis Data. International journal of advanced research in computer engineering & technology (IJARCET). Volume 6, Issue 1
[7] Mohammad Reza Moshkani, Mahdi Rousta and Yaghoub Farjami(2016). Eamining and comparing data mining-based techniques for hepatitis diagnosis. International journal of innovative research in computer and communication engineering, vol. 4, issue 1, page 750-756
[8] Nuanwan Soonthornphisaj, Supakpong Jinarat, Taweesak Tanwandee and Masayuki Numao (2007). Knowledge Discovery for Hepatitis C Virus Diagnosis: A Framework for Mining Interesting Classification Rules. International conference on intelligent computing. Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques. Volume 2, Page 171-179
[9] Piatetsky-Shapiro G. and W. J. Frawley W. J. (1991). Knowledge Discovery in Databases. AAAI/MIT Press.
[10] Pinar Yildirim (2015). Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease. International Journal of Machine Learning and Computing, Vol. 5, No. 4 Page 258-263
[11] Roslina A.H., and Noraziah A. (2010). Prediction of hepatitis prognosis using support vector machine and wrapper method. IEEE, 2209-2211
[12] Saranya A. and Seenuvasan G. (2017). A comparative study of diagnosing liver disorder disease using classification algorithm. International Journal of Computer Science and Mobile Computing. (IJCSMC )Vol. 6, Issue. 8, pg.49 – 54
[13] Sathya Devi G., (2011). Application of CART algorithm in hepatitis disease diagnosis. IEEE, 1283-1287
[14] World health organization media centre. “Hepatitis C." http://www.who.int. 2011. 5 October 2011
[15] Yaming Zhang, Yang Zhao, Xin Lin, Aibo Wang Dandan Che (2018). Modeling for the prediction of Hepatitis B incidence based on integrated online search indexes. Informatics in Medicine unlocked.volume 10, pages 143-148
[16] Zhi-Hua Zhou (2012). Ensemble Methods: Foundations and Algorithms. CRC Press
[17] Available : http://www.healthline.com/
[18] Available: http://www.medicalnewstoday

Keywords
Consistency, decision tree, hepatitis, correlation