Adjustment of an intensive care unit (ICU) data in fuzzy C-regression models
This research is an attempt to present a proper methodology in data modification by using analytical hierarchy process (AHP) technique and fuzzy c-mean (FCM) model. The continuous data were built from binary data using analytical hierarchy process (AHP). Whereas, the binary data were created from co...
| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Penerbit UTHM
2012
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| Subjects: | |
| Online Access: | http://penerbit.uthm.edu.my/ojs/index.php/JST/article/view/606 http://penerbit.uthm.edu.my/ojs/index.php/JST/article/view/606 http://eprints.uthm.edu.my/9372/1/J1865_b443b11750b32b9ff3fbdb55f47d8b1d.pdf |
| Summary: | This research is an attempt to present a proper methodology in data modification by
using analytical hierarchy process (AHP) technique and fuzzy c-mean (FCM) model.
The continuous data were built from binary data using analytical hierarchy process
(AHP). Whereas, the binary data were created from continuous data using fuzzy cmeans
(FCM) model. The models used in this research are fuzzy c-regression models
(FCRM). A case study in scale of health at an intensive care unit (ICU) ward using
the AHP, FCM model and FCRM models was carried out. There are six independent
variables involved in this study. There are four cases considered as a result of using
AHP technique and FCM model toward independent data. After comparing the four
cases, it was found that case 4 appeared to be the best model, having the lowest mean
square error (MSE). The original data have the MSE value of 97.33, while the data of
case 4 have MSE by 83.48. This means that the AHP technique can lower the MSE,
while the FCM model cannot lower the MSE in modelling scale of health in the ICU.
In other words, it can be claimed that the AHP technique can increase the accuracy
of modelling prediction. |
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