Predicting Metabolic Syndrome Using the Random Forest Method
Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify signi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/581501 |
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| author | Apilak Worachartcheewan Watshara Shoombuatong Phannee Pidetcha Wuttichai Nopnithipat Virapong Prachayasittikul Chanin Nantasenamat |
| author_facet | Apilak Worachartcheewan Watshara Shoombuatong Phannee Pidetcha Wuttichai Nopnithipat Virapong Prachayasittikul Chanin Nantasenamat |
| author_sort | Apilak Worachartcheewan |
| collection | DOAJ |
| description | Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases. |
| format | Article |
| id | doaj-art-1123acd96252425aa2036b5cc230565c |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1123acd96252425aa2036b5cc230565c2025-02-03T05:50:59ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/581501581501Predicting Metabolic Syndrome Using the Random Forest MethodApilak Worachartcheewan0Watshara Shoombuatong1Phannee Pidetcha2Wuttichai Nopnithipat3Virapong Prachayasittikul4Chanin Nantasenamat5Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandExcellence Service Center for Medical Technology and Quality Improvement, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandDepartment of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, ThailandAims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.http://dx.doi.org/10.1155/2015/581501 |
| spellingShingle | Apilak Worachartcheewan Watshara Shoombuatong Phannee Pidetcha Wuttichai Nopnithipat Virapong Prachayasittikul Chanin Nantasenamat Predicting Metabolic Syndrome Using the Random Forest Method The Scientific World Journal |
| title | Predicting Metabolic Syndrome Using the Random Forest Method |
| title_full | Predicting Metabolic Syndrome Using the Random Forest Method |
| title_fullStr | Predicting Metabolic Syndrome Using the Random Forest Method |
| title_full_unstemmed | Predicting Metabolic Syndrome Using the Random Forest Method |
| title_short | Predicting Metabolic Syndrome Using the Random Forest Method |
| title_sort | predicting metabolic syndrome using the random forest method |
| url | http://dx.doi.org/10.1155/2015/581501 |
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