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Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi

Metabolic Syndrome (MetS) is clinically defined as the presence of three out of the following five abnormalities - ihyperglycaemia, raised waist circumference, low High- Density Lipoprotein-Cholesterol, ihypertriglyceridaemia and hypertension. MetS places individuals at an unhealthy disadvantage an...

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Main Author: Habeebah Adamu , Kakudi
Format: Thesis
Published: 2019
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://studentsrepo.um.edu.my/14653/1/Habeebah.pdf
http://studentsrepo.um.edu.my/14653/2/Habeebah.pdf
http://studentsrepo.um.edu.my/14653/
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spelling my.um.stud.146532023-07-24T20:13:07Z Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi Habeebah Adamu , Kakudi QA75 Electronic computers. Computer science QA76 Computer software Metabolic Syndrome (MetS) is clinically defined as the presence of three out of the following five abnormalities - ihyperglycaemia, raised waist circumference, low High- Density Lipoprotein-Cholesterol, ihypertriglyceridaemia and hypertension. MetS places individuals at an unhealthy disadvantage and iis associated with an increased risk of non-communicable diseases such as cardiovascular disease and diabetes. Currently used non-clinical methods are not able to diagnose the risk of MetS in patients that fall very close to the clinically defined threshold. Therefore, the aim of this study is to propose and develop a novel non-clinical technique for the early risk quantification and classification of MetS refered to as genetically optimized Bayesian adaptive resonance theory mapping (GOBAM). Genetic Algorithm(GA) is used to optimize the order of sequence of the input sample and the parameters of the Bayesian ARTMAP (BAM). The "Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and well-being among school teachers in Malaysia" (CLUSTer) dataset was used to compare the performance of the proposed Genetically Optimised Bayesian ARTMAP (GOBAM) model and three other classic Adaptive Resonance Theory Mapping (ARTMAP) models –Genetic Algorithm Fuzzy ARTMAP (GAFAM), Fuzzy ARTMAP (FAM), and Bayesian ARTMAP (BAM). GOBAM achieved higher of area under the receiver operating curve, sensitivity, specificity, positive predictive value, negative predictive value, and Fscore performance metrics of 91.45%, 96.3%, 88.3% , 98.32% , 85.71% , and 96.41% respectively. The proposed GOBAM model was able to diagnose the risk of MetS efficiently with borderline MRF measurements, by utilising a novel risk prediction index that ranged between 0 and 1. 2019-07 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/14653/1/Habeebah.pdf application/pdf http://studentsrepo.um.edu.my/14653/2/Habeebah.pdf Habeebah Adamu , Kakudi (2019) Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi. PhD thesis, Universiti Malaya. http://studentsrepo.um.edu.my/14653/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Habeebah Adamu , Kakudi
Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
description Metabolic Syndrome (MetS) is clinically defined as the presence of three out of the following five abnormalities - ihyperglycaemia, raised waist circumference, low High- Density Lipoprotein-Cholesterol, ihypertriglyceridaemia and hypertension. MetS places individuals at an unhealthy disadvantage and iis associated with an increased risk of non-communicable diseases such as cardiovascular disease and diabetes. Currently used non-clinical methods are not able to diagnose the risk of MetS in patients that fall very close to the clinically defined threshold. Therefore, the aim of this study is to propose and develop a novel non-clinical technique for the early risk quantification and classification of MetS refered to as genetically optimized Bayesian adaptive resonance theory mapping (GOBAM). Genetic Algorithm(GA) is used to optimize the order of sequence of the input sample and the parameters of the Bayesian ARTMAP (BAM). The "Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and well-being among school teachers in Malaysia" (CLUSTer) dataset was used to compare the performance of the proposed Genetically Optimised Bayesian ARTMAP (GOBAM) model and three other classic Adaptive Resonance Theory Mapping (ARTMAP) models –Genetic Algorithm Fuzzy ARTMAP (GAFAM), Fuzzy ARTMAP (FAM), and Bayesian ARTMAP (BAM). GOBAM achieved higher of area under the receiver operating curve, sensitivity, specificity, positive predictive value, negative predictive value, and Fscore performance metrics of 91.45%, 96.3%, 88.3% , 98.32% , 85.71% , and 96.41% respectively. The proposed GOBAM model was able to diagnose the risk of MetS efficiently with borderline MRF measurements, by utilising a novel risk prediction index that ranged between 0 and 1.
format Thesis
author Habeebah Adamu , Kakudi
author_facet Habeebah Adamu , Kakudi
author_sort Habeebah Adamu , Kakudi
title Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
title_short Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
title_full Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
title_fullStr Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
title_full_unstemmed Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi
title_sort computational intelligence approach for classification and risk quantification of metabolic syndrome / habeebah adamu kakudi
publishDate 2019
url http://studentsrepo.um.edu.my/14653/1/Habeebah.pdf
http://studentsrepo.um.edu.my/14653/2/Habeebah.pdf
http://studentsrepo.um.edu.my/14653/
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score 13.211869

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