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Using feature selection as accuracy benchmarking in clinical data mining.

Automated prediction of new patients’ disease diagnosis based on data mining analysis on historical data is proven to be an extremely useful tool in the medical innovation. There are several studies focusing on this particular aspect. The objective of this study is two-fold. First, we look into th...

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Main Authors: Hossain, Jafreen, Mohd. Sani, Nor Fazlida, Mustapha, Aida, Affendey, Lilly Suriani
Format: Article
Language:English
English
Published: Science Publications 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30669/1/Using%20feature%20selection%20as%20accuracy%20benchmarking%20in%20clinical%20data%20mining.pdf
http://psasir.upm.edu.my/id/eprint/30669/
http://thescipub.com/issue-jcs/9/7
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spelling my.upm.eprints.306692015-09-11T03:48:08Z http://psasir.upm.edu.my/id/eprint/30669/ Using feature selection as accuracy benchmarking in clinical data mining. Hossain, Jafreen Mohd. Sani, Nor Fazlida Mustapha, Aida Affendey, Lilly Suriani Automated prediction of new patients’ disease diagnosis based on data mining analysis on historical data is proven to be an extremely useful tool in the medical innovation. There are several studies focusing on this particular aspect. The objective of this study is two-fold. First, we look into three different classifiers, which are the Naïve Bayes, Multilayer Perceptron (MLP) and Decision Tree J48 to predict the diagnosis results. Next, we investigate the effects of feature selection in such experiments. We also compare the experimental results with the study of Comparative Disease Profile (CDP) using the same dataset. Results have shown that the Naive Bayes provides the best result in terms of accuracy in our experiments and in comparison with CDP. However, we suggest using Multilayer Perceptron since the variables used in our experiments are inter-dependent among each other. In addition, MLP has shown better accuracy than CDP. Science Publications 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30669/1/Using%20feature%20selection%20as%20accuracy%20benchmarking%20in%20clinical%20data%20mining.pdf Hossain, Jafreen and Mohd. Sani, Nor Fazlida and Mustapha, Aida and Affendey, Lilly Suriani (2013) Using feature selection as accuracy benchmarking in clinical data mining. Journal of Computer Science, 9 (7). pp. 883-888. ISSN 1549-3636 http://thescipub.com/issue-jcs/9/7 10.3844/jcssp.2013.883.888 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description Automated prediction of new patients’ disease diagnosis based on data mining analysis on historical data is proven to be an extremely useful tool in the medical innovation. There are several studies focusing on this particular aspect. The objective of this study is two-fold. First, we look into three different classifiers, which are the Naïve Bayes, Multilayer Perceptron (MLP) and Decision Tree J48 to predict the diagnosis results. Next, we investigate the effects of feature selection in such experiments. We also compare the experimental results with the study of Comparative Disease Profile (CDP) using the same dataset. Results have shown that the Naive Bayes provides the best result in terms of accuracy in our experiments and in comparison with CDP. However, we suggest using Multilayer Perceptron since the variables used in our experiments are inter-dependent among each other. In addition, MLP has shown better accuracy than CDP.
format Article
author Hossain, Jafreen
Mohd. Sani, Nor Fazlida
Mustapha, Aida
Affendey, Lilly Suriani
spellingShingle Hossain, Jafreen
Mohd. Sani, Nor Fazlida
Mustapha, Aida
Affendey, Lilly Suriani
Using feature selection as accuracy benchmarking in clinical data mining.
author_facet Hossain, Jafreen
Mohd. Sani, Nor Fazlida
Mustapha, Aida
Affendey, Lilly Suriani
author_sort Hossain, Jafreen
title Using feature selection as accuracy benchmarking in clinical data mining.
title_short Using feature selection as accuracy benchmarking in clinical data mining.
title_full Using feature selection as accuracy benchmarking in clinical data mining.
title_fullStr Using feature selection as accuracy benchmarking in clinical data mining.
title_full_unstemmed Using feature selection as accuracy benchmarking in clinical data mining.
title_sort using feature selection as accuracy benchmarking in clinical data mining.
publisher Science Publications
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/30669/1/Using%20feature%20selection%20as%20accuracy%20benchmarking%20in%20clinical%20data%20mining.pdf
http://psasir.upm.edu.my/id/eprint/30669/
http://thescipub.com/issue-jcs/9/7
_version_ 1643830128499752960
score 13.211869

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