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...

Full description

Saved in:

Bibliographic Details
Main Author:	Rusiman, Mohd. Saifullah
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/32330/1/Mohd.Saifullah%20RusimanPFS2012.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.32330
record_format	uketd_dc
spelling	my-utm-ep.323302017-08-01T01:51:51Z On the use of fuzzy C-regression truncated models for health indicator in intensive care unit 2012 Rusiman, Mohd. Saifullah QA Mathematics 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, NEW F . The various values of constant, k (k = 0.1, 0.2, ..., 8) in generalized distance error and various values of fuzzifier, w (1< 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 truncated absolute 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 case study in health indicator (simplified acute physiology score II (SAPS II 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 II 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 fuzzy 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. 2012 Thesis http://eprints.utm.my/id/eprint/32330/ http://eprints.utm.my/id/eprint/32330/1/Mohd.Saifullah%20RusimanPFS2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70069?queryType=vitalDismax&query=On+the+use+of+fuzzy+C-regression+truncated+models+for+health+indicator+in+intensive+care+unit&public=true phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Rusiman, Mohd. Saifullah On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
description	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, NEW F . The various values of constant, k (k = 0.1, 0.2, ..., 8) in generalized distance error and various values of fuzzifier, w (1< 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 truncated absolute 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 case study in health indicator (simplified acute physiology score II (SAPS II 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 II 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 fuzzy 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.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rusiman, Mohd. Saifullah
author_facet	Rusiman, Mohd. Saifullah
author_sort	Rusiman, Mohd. Saifullah
title	On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
title_short	On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
title_full	On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
title_fullStr	On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
title_full_unstemmed	On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
title_sort	on the use of fuzzy c-regression truncated models for health indicator in intensive care unit
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2012
url	http://eprints.utm.my/id/eprint/32330/1/Mohd.Saifullah%20RusimanPFS2012.pdf
_version_	1747815976695496704

On the use of fuzzy C-regression truncated models for health indicator in intensive care unit

Similar Items