Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP
Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims t...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2019
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| _version_ | 1825722196167753728 |
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| author | Kakudi, Habeebah Adamu Loo, Chu Kiong Moy, Foong Ming Masuyama, Naoki Pasupa, Kitsuchart |
| author_facet | Kakudi, Habeebah Adamu Loo, Chu Kiong Moy, Foong Ming Masuyama, Naoki Pasupa, Kitsuchart |
| author_sort | Kakudi, Habeebah Adamu |
| collection | UM |
| description | Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. © 2013 IEEE. |
| first_indexed | 2024-03-06T06:01:57Z |
| format | Article |
| id | um.eprints-24155 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:57Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-241552020-04-06T15:35:53Z http://eprints.um.edu.my/24155/ Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP Kakudi, Habeebah Adamu Loo, Chu Kiong Moy, Foong Ming Masuyama, Naoki Pasupa, Kitsuchart QA75 Electronic computers. Computer science R Medicine Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. © 2013 IEEE. Institute of Electrical and Electronics Engineers 2019 Article PeerReviewed Kakudi, Habeebah Adamu and Loo, Chu Kiong and Moy, Foong Ming and Masuyama, Naoki and Pasupa, Kitsuchart (2019) Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP. IEEE Access, 7. pp. 8437-8453. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2018.2880224 <https://doi.org/10.1109/ACCESS.2018.2880224>. https://doi.org/10.1109/ACCESS.2018.2880224 doi:10.1109/ACCESS.2018.2880224 |
| spellingShingle | QA75 Electronic computers. Computer science R Medicine Kakudi, Habeebah Adamu Loo, Chu Kiong Moy, Foong Ming Masuyama, Naoki Pasupa, Kitsuchart Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title | Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title_full | Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title_fullStr | Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title_full_unstemmed | Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title_short | Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP |
| title_sort | diagnosing metabolic syndrome using genetically optimised bayesian artmap |
| topic | QA75 Electronic computers. Computer science R Medicine |
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