Comparison of Accuracy in Evaluation of Obesity Utilizing Fuzzy Rule-based Systems and Multinomial Logistic Regression of Workers in Mae Moh Power Plant, Lampang Province
Abstract
This study aimed to compare the accuracy in evaluation of obesity between utilizing Fuzzy Rule-based Systems and Multinomial Logistic Regression of workers in Mae Moh Power Plant, Lampang Province. The sample group consisted of 237 power plant workers in Mae Moh who attend the annual health check at Mae Moh Division of Medical and Health. The data were analyzed by using Factor Analysis and then using Fuzzy Rule-based Systems and Multinomial Logistic Regression. The accuracy in evaluation of obesity were checked. The results of Multinomial Logistic Regression analysis can be evaluated at 51%. The accuracy in group of obese, normal and overweight are 82%, 52% and 0%, respectively. Fuzzy Rule-based Systems has 227 rule and can be evaluated at 78%. The accuracy in group of obese, normal and overweight are 100%, 72% and 48%, respectively. The results of evaluation of obesity utilizing Fuzzy Rule-based Systems are more accurate. Keywords : obesity, Fuzzy Logic, Fuzzy Rule-based Systems, Multinomial Logistic RegressionReferences
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Chulalongkorn University Press. (in Thai)
https://www.scribd.com/document/82775047/Fuzzy-Logic 3 กันยายน 2560
Damapong, S. (2008). Manual…Normal Weight Organization. (1). Bangkok: The War Veterans Organization of
Thailand Printing House. (in Thai)
Demir, M., Basaran, M. & Simonetti, B. (2016). Determining factors affecting healthcare service satisfaction
utilizing fuzzy rule-based systems. Journal of Applied Statistics, 43(13), 2474-2489.
Folk Doctor Thailand. Abdominal Obesity…Danger. Retrieved October 10, 2015, from https://goo.gl/ZImnWV.
(in Thai)
Health Station. (2015). Thai Health 2015 11 Obesity Indicators. Retrieved October 10, 2015, from
http://www.healthstation.in.th/action/viewarticle/827. (in Thai)
Jiawiwatkul, U. (2010). Multiple logistic. (2). In Research Zone Phrase 14th. (pp. 2-53). Bangkok: National
Research Council of Thailand. (in Thai)
Nithiyanan, W. (2011). Obesity and Abdominal Obesity. (1). Bangkok: Sukhumvit Media Marketing Co.,Ltd.
(in Thai)
Office of the Royal Society. (2015). Dictionary of Statistics the Royal Society. (1). Bangkok: Office of the Royal
Society. (in Thai)
Pongsapakdee, W. (1998). Analysis of Categorical Data : Theories and Applications with GLIM and SPSS/FW.
(2). Nakhon Pathom: Silpakorn University Printinghouse. (in Thai)
Rawinij, P. & Sattham, A. (2016). Factor Associated with the Metabolic Syndrome in Chamab Sub-
district, Wang Noi District, Ayutthaya Province. APMEIT Journals, 5(4), 33-47. (in Thai)
Suwaphan, A. Fuzzy Logic. Retrieved September 3, 2017, from https://goo.gl/g1SxGn. (in Thai)
Thai Health Promotion Foundation. Abdominal Obesity. Retrieved October 10, 2015, from
http://resource.thaihealth.or.th/taxonomy/term/1627. (in Thai)
Thairath Online. Abdominal Obesity…Disaster Warning Health Hazards. Retrieved October 10, 2015, from
http://www.thairath.co.th/content/188331. (in Thai)
Thoobjeen, S. Rattanasiriwongwut, M. & Tiantong, M. (2017). Fuzzy Set Greading for Student’s Learning
Assesment. Thai Journal of Science and Technology, 25(3), 388-400. (in Thai)
Wanijbancha, K. (2009). Analysis Data for Multiple Variables with SPSS for Windows. (4). Bangkok:
Chulalongkorn University Press. (in Thai)
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Published
2018-07-18
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Research Article
