A comparison of normalization techniques in predicting dengue outbreak
In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is in...
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| Main Authors: | , |
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| Format: | Book Section |
| Language: | English |
| Published: |
IEEE Computer Society
2010
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/3163/1/z1%5B1%5D.pdf http://repo.uum.edu.my/3163/ http://www.icif.org/ |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of
dengue cases is increasing rapidly in Malaysia, more work
need to be done in order to prevent this situation become
critical. This includes work on predicting future dengue
outbreak. This paper investigates the use of three
normalization techniques in predicting dengue outbreak; Min-
Max, Z-Score and Decimal Point Normalization. These
techniques are incorporated in the LS-SVM and Neural
Network (NNM) prediction model respectively. Comparisons
of results are made based on prediction accuracy and mean
squared error (MSE). Results obtained indicate that the LSSVM is a better prediction model as compared to the NNM. |
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