An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study

Lack of diagnosis for diabetes often transpire in some ASEAN countries with relatively diminutive doctor to patient ratio.Essentially,it is believed that a systematic framework to predict diabetes risk factors is crucial for refining diagnostics and improving accuracy. However,there is the issue of...

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Main Author:	Sutanto, Daniel Hartono
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	Q Science (General) QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/23340/1/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf http://eprints.utem.edu.my/id/eprint/23340/2/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf
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institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA76 Computer software
spellingShingle	Q Science (General) QA76 Computer software Sutanto, Daniel Hartono An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
description	Lack of diagnosis for diabetes often transpire in some ASEAN countries with relatively diminutive doctor to patient ratio.Essentially,it is believed that a systematic framework to predict diabetes risk factors is crucial for refining diagnostics and improving accuracy. However,there is the issue of noisy dataset detected as incomplete data and the outlier class problem that affects sampling bias.Existing frameworks were deemed difficult in identifying the critical risk factors of diabetes;some of which were considerably inaccurate and consume substantial computation time.The purpose of this study is to develop a suitable framework for predicting diabetes risks.From a complete blood test,the framework can predict and classify the output of either having diabetes risk or no diabetes risk.A Diabetes Risk Prediction Framework (DRPF) was developed from the literature review and case studies were afterwards conducted in three private hospitals in Semarang.Analyses were conducted to find a suitable component of the framework—due to lack of comparison and analysis on the combination of feature selection and classification algorithm.DRPF comprises four main sections: pre-processing,outlier detection,risk weighting,and learning. Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. In the learning section,Support Vector Machine and Artificial Neural Network were selected as suitable classification algorithms,while Gradient Boosted Tree was employed to interpret the rule based on the black box classifiers.Testing the framework involved Pima Indian Dataset as public dataset and Semarang Hospital Dataset as private dataset (800 patients’ data).In validating the DRPF,four case studies investigated Subject Matter Expert (SME) groups based on the agreement level.The questionnaire consists of a DRPF component,implementation of DRPF,and viability of DRPF.DRPF components were validated by the SMEs,whereby the group ascertained five highest risk factors:HbA1c,systole/diastole,blood glucose,and creatinine and blood urea nitrogen that were assigned by attribute weighting.Results from the questionnaire revealed an average agreement level of 80%. In conclusion,DRPF is implementable as prototype and has been highly accepted by Indonesian practitioners as aid for the diagnostics of diabetes.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sutanto, Daniel Hartono
author_facet	Sutanto, Daniel Hartono
author_sort	Sutanto, Daniel Hartono
title	An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
title_short	An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
title_full	An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
title_fullStr	An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
title_full_unstemmed	An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study
title_sort	improved diabetes risk prediction framework : an indonesian case study
granting_institution	UTeM
granting_department	Faculty Of Information And Communication Technology
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23340/1/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf http://eprints.utem.edu.my/id/eprint/23340/2/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf
_version_	1747834039038902272
spelling	my-utem-ep.233402022-02-07T11:47:47Z An Improved Diabetes Risk Prediction Framework : An Indonesian Case Study 2018 Sutanto, Daniel Hartono Q Science (General) QA76 Computer software Lack of diagnosis for diabetes often transpire in some ASEAN countries with relatively diminutive doctor to patient ratio.Essentially,it is believed that a systematic framework to predict diabetes risk factors is crucial for refining diagnostics and improving accuracy. However,there is the issue of noisy dataset detected as incomplete data and the outlier class problem that affects sampling bias.Existing frameworks were deemed difficult in identifying the critical risk factors of diabetes;some of which were considerably inaccurate and consume substantial computation time.The purpose of this study is to develop a suitable framework for predicting diabetes risks.From a complete blood test,the framework can predict and classify the output of either having diabetes risk or no diabetes risk.A Diabetes Risk Prediction Framework (DRPF) was developed from the literature review and case studies were afterwards conducted in three private hospitals in Semarang.Analyses were conducted to find a suitable component of the framework—due to lack of comparison and analysis on the combination of feature selection and classification algorithm.DRPF comprises four main sections: pre-processing,outlier detection,risk weighting,and learning. Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. In the learning section,Support Vector Machine and Artificial Neural Network were selected as suitable classification algorithms,while Gradient Boosted Tree was employed to interpret the rule based on the black box classifiers.Testing the framework involved Pima Indian Dataset as public dataset and Semarang Hospital Dataset as private dataset (800 patients’ data).In validating the DRPF,four case studies investigated Subject Matter Expert (SME) groups based on the agreement level.The questionnaire consists of a DRPF component,implementation of DRPF,and viability of DRPF.DRPF components were validated by the SMEs,whereby the group ascertained five highest risk factors:HbA1c,systole/diastole,blood glucose,and creatinine and blood urea nitrogen that were assigned by attribute weighting.Results from the questionnaire revealed an average agreement level of 80%. In conclusion,DRPF is implementable as prototype and has been highly accepted by Indonesian practitioners as aid for the diagnostics of diabetes. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23340/ http://eprints.utem.edu.my/id/eprint/23340/1/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf text en public http://eprints.utem.edu.my/id/eprint/23340/2/An%20Improved%20Diabetes%20Risk%20Prediction%20Framework%20An%20Indonesian%20Case%20Study.pdf text en validuser http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113297 phd doctoral UTeM Faculty Of Information And Communication Technology 1. Advisor, T.R., 2006. Sample Size Table. Available at: http://researchadvisors.com/tools/SampleSize.htm [Accessed 1 June 2015]. 2. Aha, D.W. & Kibler, D., 1988. Heart Disease Data Set. Available at: https://archive.ics.uci.edu/ml/datasets/Heart+Disease [Accessed 15 December 2015]. 3. 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