Hybrid modelling using decision tree and ordered regression: an application to health sciences research

With the increasing complexity of healthcare data, there is a need for more advanced and integrative predictive modelling techniques. This thesis presents a novel hybrid methodology integrating Decision Trees and Ordinal Regression using the R-syntax. The study objectives include the development of...

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Main Author:	Shahzad, Hazik
Format:	Thesis
Language:	English
Published:	2024
Subjects:	R Medicine
Online Access:	http://eprints.usm.my/60708/1/HAZIK%20BIN%20SHAHZAD-FINAL%20THESIS%20P-SGD001120%28R%29-E.pdf
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id	my-usm-ep.60708
record_format	uketd_dc
spelling	my-usm-ep.607082024-07-24T03:45:41Z Hybrid modelling using decision tree and ordered regression: an application to health sciences research 2024-01 Shahzad, Hazik R Medicine RA0421 Public health. Hygiene. Preventive Medicine RA440-440.87 Study and teaching. Research With the increasing complexity of healthcare data, there is a need for more advanced and integrative predictive modelling techniques. This thesis presents a novel hybrid methodology integrating Decision Trees and Ordinal Regression using the R-syntax. The study objectives include the development of the hybrid method, measuring its efficacy and efficiency, validating its performance through predictive classification analysis, and optimising parameter estimates for optimised statistical inferences. The hybrid methodology uses decision trees, facilitated by visualisation tools, to identify influential factors that shape the model’s predictions. The bootstrap resampling method boosts the data set’s resilience and facilitates the development of an ordinal regression model. The introduction of the hybrid accuracy index enhances interpretability. The hybrid methodology is employed in two health sciences scenarios. In Case I, it predicts the frequency of toothbrushing among students, and in Case II, it predicts diabetic status using oral health indicators. This study introduces a hybrid method that generates numerical results along with graphical visualisation, enhancing the accuracy and efficiency of the parameter estimates. The findings of this study contribute to the development of an innovative approach to transforming predictive modelling in healthcare, contributing to future research methodologies for more precise decision-making. 2024-01 Thesis http://eprints.usm.my/60708/ http://eprints.usm.my/60708/1/HAZIK%20BIN%20SHAHZAD-FINAL%20THESIS%20P-SGD001120%28R%29-E.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Perubatan
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	R Medicine R Medicine R Medicine
spellingShingle	R Medicine R Medicine R Medicine Shahzad, Hazik Hybrid modelling using decision tree and ordered regression: an application to health sciences research
description	With the increasing complexity of healthcare data, there is a need for more advanced and integrative predictive modelling techniques. This thesis presents a novel hybrid methodology integrating Decision Trees and Ordinal Regression using the R-syntax. The study objectives include the development of the hybrid method, measuring its efficacy and efficiency, validating its performance through predictive classification analysis, and optimising parameter estimates for optimised statistical inferences. The hybrid methodology uses decision trees, facilitated by visualisation tools, to identify influential factors that shape the model’s predictions. The bootstrap resampling method boosts the data set’s resilience and facilitates the development of an ordinal regression model. The introduction of the hybrid accuracy index enhances interpretability. The hybrid methodology is employed in two health sciences scenarios. In Case I, it predicts the frequency of toothbrushing among students, and in Case II, it predicts diabetic status using oral health indicators. This study introduces a hybrid method that generates numerical results along with graphical visualisation, enhancing the accuracy and efficiency of the parameter estimates. The findings of this study contribute to the development of an innovative approach to transforming predictive modelling in healthcare, contributing to future research methodologies for more precise decision-making.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shahzad, Hazik
author_facet	Shahzad, Hazik
author_sort	Shahzad, Hazik
title	Hybrid modelling using decision tree and ordered regression: an application to health sciences research
title_short	Hybrid modelling using decision tree and ordered regression: an application to health sciences research
title_full	Hybrid modelling using decision tree and ordered regression: an application to health sciences research
title_fullStr	Hybrid modelling using decision tree and ordered regression: an application to health sciences research
title_full_unstemmed	Hybrid modelling using decision tree and ordered regression: an application to health sciences research
title_sort	hybrid modelling using decision tree and ordered regression: an application to health sciences research
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Perubatan
publishDate	2024
url	http://eprints.usm.my/60708/1/HAZIK%20BIN%20SHAHZAD-FINAL%20THESIS%20P-SGD001120%28R%29-E.pdf
_version_	1811772845045317632

Hybrid modelling using decision tree and ordered regression: an application to health sciences research

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