On the use of fuzzy c-regression truncated models for health indicator in intensive care unit
Two new techniques for clustering data, namely the fuzzy c-regression truncated models (FCRTM) and fuzzy c-regression least quartile difference (LQD) models (FCRLM) were proposed in this thesis in analyzing a nonlinear model. These new models include their functions, the estimation techniques and th...
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| Format: | Thesis |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4668/ http://eprints.uthm.edu.my/4668/3/mohd_saifullah_rusiman.pdf |
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| Summary: | Two new techniques for clustering data, namely the fuzzy c-regression
truncated models (FCRTM) and fuzzy c-regression least quartile difference (LQD)
models (FCRLM) were proposed in this thesis in analyzing a nonlinear model. These
new models include their functions, the estimation techniques and the explanation of
the five procedures. The stepwise method was used for variable selection in the
FCRTM and FCRLM models. The number of clusters was determined using the
compactness-to-separation ratio, F,,, . The various values of constant, k (k = 0.1,
0.2, ..., a) in generalized distance error and various values of fuzzifier, w (I< w <3)
were used in order to find the lowest mean square error (MSE). Then, the data were
grouped based on cluster and analyzed using truncatedabsolute residual (TAR) and
the least quartile difference (LQD) technique. The FCRTM and FCRLM models
were tested on the simulated data and these models can approximate the given
nonlinear system with the highest accuracy. A casC study in health indicator
(simplified acute physiology score I1 (SAPS I1 score) when discharge from hospital)
at the intensive care unit (ICU) ward was carried out using the FCRTM and FCRLM
models as mentioned above. Eight cases of data involving six independent variables
(sex, race, organ failure, comorbid disease; mechanical ventilation and SAPS I1 score
when admitted to hospital) with different combinations of variable types in each case
were considered to find the best modified data. The comparisons among the fizzy cmeans
(FCM) model, fuzzy c-regression models (FCRM), multiple linear regression
model, Cox proportional-hazards model, fuzzy linear regression model (FLRM),
fuzzy least squares regression model (FLSRM), new affine Takagi Sugeno fuzzy
models, FCRTM models and FCRLM models were carried out to find the best model
by using the mean square error (MSE), root mean square error (RMSE), mean
absolute error (MAE) and mean absolute percentage error (MAPE). The results
showed that the FCRTM models were found to be the best model, having the lowest
MSE, RMSE, MAE and MAPE. This new modelling technique could be proposed as
one of the best models in analyzing mainly a complex system. Hence, the health
indicator in the ICU ward could be monitored by managing six independent variables
and other management quality variables in the hospital management. |
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