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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Science Publications
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30669/ http://psasir.upm.edu.my/id/eprint/30669/1/Using%20feature%20selection%20as%20accuracy%20benchmarking%20in%20clinical%20data%20mining.pdf |
| _version_ | 1848846744055447552 |
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| author | Hossain, Jafreen Mohd. Sani, Nor Fazlida Mustapha, Aida Affendey, Lilly Suriani |
| author_facet | Hossain, Jafreen Mohd. Sani, Nor Fazlida Mustapha, Aida Affendey, Lilly Suriani |
| author_sort | Hossain, Jafreen |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T09:07:34Z |
| format | Article |
| id | upm-30669 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T09:07:34Z |
| publishDate | 2013 |
| publisher | Science Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| spellingShingle | Hossain, Jafreen Mohd. Sani, Nor Fazlida Mustapha, Aida Affendey, Lilly Suriani Using feature selection as accuracy benchmarking in clinical data mining. |
| title | 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_short | Using feature selection as accuracy benchmarking in clinical data mining. |
| title_sort | using feature selection as accuracy benchmarking in clinical data mining. |
| url | http://psasir.upm.edu.my/id/eprint/30669/ http://psasir.upm.edu.my/id/eprint/30669/ http://psasir.upm.edu.my/id/eprint/30669/ http://psasir.upm.edu.my/id/eprint/30669/1/Using%20feature%20selection%20as%20accuracy%20benchmarking%20in%20clinical%20data%20mining.pdf |