Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea

This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. This system can provide better prediction accuracies and speedier medical analyses in order to...

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Main Author:	Sim, Doreen Ying Ying
Format:	Thesis
Language:	English
Published:	2018
Subjects:	BF Psychology
Online Access:	http://ir.unimas.my/id/eprint/26594/1/Doreen%20Sim.pdf
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id	my-unimas-ir.26594
record_format	uketd_dc
spelling	my-unimas-ir.265942023-07-27T07:05:46Z Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea 2018 Sim, Doreen Ying Ying BF Psychology This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. This system can provide better prediction accuracies and speedier medical analyses in order to help medical doctors in the earlier clinical diagnoses of Obstructive Sleep Apnea, i.e. OSA. The prediction algorithms developed are based on the OSA datasets collected mainly from the public hospitals in Selangor, Malaysia. The proposed OSA questionnaires have been newly designed after the data collection has been completed. The newly proposed and designed OSA questionnaires are customizable to best fit for Malaysians and have significant differences with the internationally standardized OSA questionnaires since these questionnaires are tailor-made based on the raw data collected within populations in Malaysia only. The parameters involved in the prediction algorithms developed are based on common OSA risk factors and visual-inspected variables found in the patients’ records in the OSA datasets collected. As benchmarked comparisons and contrasts, the performance of the computational intelligent system developed in this research is testified with quite a number of standard online databases downloaded mainly from the University of California Irvine data repositories. This is to ensure the generalized performance, flexible strengths and robustness of the OSA prediction system developed. Research outputs showed that there is a significant prediction improvement of the algorithms and system developed based on all types of datasets being accessed against the classical boosting approaches on the same datasets. There is a stepwise prediction improvement from the classical approach of Boosted Decision Trees to the developed Boosted Pruned-Decision Trees and then to Boosted Pruned-Association-Rule-Minded-Decision Trees. The developed prediction iv algorithms have been proven to help medical doctors with earlier clinical diagnoses on OSA cases, especially in Malaysia. Universiti Malaysia Sarawak (UNIMAS) 2018 Thesis http://ir.unimas.my/id/eprint/26594/ http://ir.unimas.my/id/eprint/26594/1/Doreen%20Sim.pdf text en validuser phd doctoral Universiti Malaysia Sarawak (UNIMAS) Faculty of Cognitive Sciences and Human Development
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	BF Psychology
spellingShingle	BF Psychology Sim, Doreen Ying Ying Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
description	This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. This system can provide better prediction accuracies and speedier medical analyses in order to help medical doctors in the earlier clinical diagnoses of Obstructive Sleep Apnea, i.e. OSA. The prediction algorithms developed are based on the OSA datasets collected mainly from the public hospitals in Selangor, Malaysia. The proposed OSA questionnaires have been newly designed after the data collection has been completed. The newly proposed and designed OSA questionnaires are customizable to best fit for Malaysians and have significant differences with the internationally standardized OSA questionnaires since these questionnaires are tailor-made based on the raw data collected within populations in Malaysia only. The parameters involved in the prediction algorithms developed are based on common OSA risk factors and visual-inspected variables found in the patients’ records in the OSA datasets collected. As benchmarked comparisons and contrasts, the performance of the computational intelligent system developed in this research is testified with quite a number of standard online databases downloaded mainly from the University of California Irvine data repositories. This is to ensure the generalized performance, flexible strengths and robustness of the OSA prediction system developed. Research outputs showed that there is a significant prediction improvement of the algorithms and system developed based on all types of datasets being accessed against the classical boosting approaches on the same datasets. There is a stepwise prediction improvement from the classical approach of Boosted Decision Trees to the developed Boosted Pruned-Decision Trees and then to Boosted Pruned-Association-Rule-Minded-Decision Trees. The developed prediction iv algorithms have been proven to help medical doctors with earlier clinical diagnoses on OSA cases, especially in Malaysia.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sim, Doreen Ying Ying
author_facet	Sim, Doreen Ying Ying
author_sort	Sim, Doreen Ying Ying
title	Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
title_short	Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
title_full	Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
title_fullStr	Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
title_full_unstemmed	Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
title_sort	development of a prediction algorithm using boosted decision trees for earlier diagnoses on obstructive sleep apnea
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Cognitive Sciences and Human Development
publishDate	2018
url	http://ir.unimas.my/id/eprint/26594/1/Doreen%20Sim.pdf
_version_	1783728315377385472

Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea

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