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An Ensemble approach on Missing Value Handling in Hepatitis Disease Dataset

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Sridevi Radhakrishnan, D. Shanmuga Priyaa

Sridevi Radhakrishnan and Shanmuga D Priyaa. Article: An Ensemble approach on Missing Value Handling in Hepatitis Disease Dataset. International Journal of Computer Applications 130(17):23-27, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Sridevi Radhakrishnan and D. Shanmuga Priyaa},
	title = {Article: An Ensemble approach on Missing Value Handling in Hepatitis Disease Dataset},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {130},
	number = {17},
	pages = {23-27},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The Major work in data pre-processing is handling Missing value imputation in Hepatitis Disease Diagnosis which is one of the primary stage in data mining. Many health datasets are typically imperfect. Just removing the cases from the original datasets can fetch added problems than elucidations. A appropriate technique for missing value imputation can assist to generate high-quality datasets for enhanced scrutinizing in clinical trials. This paper investigates the exploit of a machine learning technique as a missing value imputation process for incomplete Hepatitis data. Mean/mode imputation, ID3 algorithm imputation, decision tree imputation and proposed bootstrap aggregation based imputation are used as missing value imputation and the resultant datasets are classified using KNN. The experiment reveals that classifier performance is enhanced when the Bagging based imputation algorithm is used to foresee missing attribute values.


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data mining, prediction, knn, imputation, missing values, bagging, bootstrap