Management of Obesity Using Fuzzy Analytic Hierarchy Process

Authors

  • Norpah Mahat University Technology MARA Perlis Branch, 02600 Arau, Perlis
  • Fatin Thaqifah Ariff University Technology MARA Perlis Branch, 02600 Arau, Perlis
  • Siti Sarah Raseli University Technology MARA Perlis Branch, 02600 Arau, Perlis

Keywords:

Analytical Hierarchy Process, Fuzzy, Peninsular Malaysia, Management, Obesity

Abstract

Obesity has been linked to several heart diseases. Unfortunately, Malaysians today are the most overweight or obese people among Asian nations. That is, half of the population is overweight. There are serious social consequences. Therefore, specific and serious actions are needed to reduce or eliminate this threat. The rapid increase in obesity rates has directed the link with the factors such as low physical activities, ease of transportation, food intake and emotional factor. Thus, this study aims at identifying the most significant factors affecting obesity in Peninsular Malaysia. The factors that are detected to be the cause of obesity can help manage individuals with obesity problems. The fuzzy Analytic Hierarchy Process (F-AHP) approach was used for the identification and ranking of each factor. The method adopted was one of the most reliable methods for both identification and ranking of the most significant factors affecting obesity. The study selected seven respondents and applied the calculations. The calculations consist of seven steps. Initially, the goals and criteria to construct the hierarchy process were determined. This was followed by the distribution of a questionnaire to the respondents using the linguistic scale. After that, two parameters were calculated, these include geometric mean weight and fuzzy weight. Lastly, the fuzzy weight and all the calculations were defuzzified and normalized. The results show the factors that affect obesity the most in Peninsular Malaysia is physical inactivity with a weight of 41.6% after being normalized. Physical inactivity has been ranked the first factor, food intake ranked second, the third rank is an emotional factor and lastly is technology. The precise identification and ranking are very meaningful to the management of the obesity problem. Firstly, there is a need to increase healthy physical activity. Secondly, they need to change their diet according to experts. Lastly, they need to manage their emotions and technology well. This study will enable the public to better understand the causes of obesity and help them to make the proper health adjustment to their lifestyle to achieve better health. The findings from this study also will assist the government in formulating a national health plan to tackle obesity among the public. 

References

Bernadac, M., Ghori, A., Cannenterre, J., Mou, N., & Moul, S. (2019). Children’s obesity in the United States and the actions of the media. International Conference of Scientific Paper, 21-28.

Bauman, A., Bull, F., Chey, T., Craig, C. L., Ainsworth, B. E., Sallis, J. F. & Pratt, M. (2009). Theinternational prevalence study on physical activity: results from 20 countries. International journalof behavioral nutrition and physical activity, 6(1), 21.

Buckley, J.J. & Uppuluri, V. R. R. (1987). Fuzzy hierarchical analysis. In Uncertainty in Risk Assessment, Risk Management, and Decision Making. Sringer, Boston, MA., 389-401.

Çebi, A., & Karal, H. (2017). An Application of Fuzzy Analytic Hierarchy Process (FAHP) for Evaluating Students' Project. Educational Research and Reviews, 12(3), 120-132.

Chandani, S., Pallavi, V., Madan, P., Om, P.K. & Mukundha (2020). Risk Factors Associated with Overweight and Obesity among Reproductive Aged (15-49) Years Females in Urban Areas of

Rajbiraj Municipality, Saptari. Advances in Obesity, Weight Management & Control, 10(3), 84 -88.

Erica, L.K. & Steven, L.G. (2016). United States Adolescents’ Television, Computer, Videogame, Smartphone and Tablet use: Associations with Sugary Drinks, Sleep, Physical Activity and Obesity.

The Journal of Pediatrics, 82, 144 - 149.

Garcia, G., Sunil, T. S., & Hinojosa, P. (2012). The fast food and obesity link: consumption patternsand severity of obesity. Obesity surgery, 22(5), 810-818.

Hazizi, A. S., Aina Mardiah, B., Mohd Nasir, M. T., Zaitun, Y., Hamid Jan, J. M., & Tabata, I. (2012). Accelerometer-determined physical activity level among government employees in Penang, Malaysia. Malaysian journal of nutrition, 18(1).

Hemmingsson, E. (2014). A new model of the role of psychological and emotional distress in promotingobesity: conceptual review with implications for treatment and prevention. Obesity Reviews, 15(9),769-779.

Jonathan, Q. P. (2018). Definitions, Classification and Epidemiology of Obesity. Endocrinology Book.

Jorm, A. F., Korten, A. E., Christensen, H., Jacomb, P. A., Rodgers, B., & Parslow, R. A. (2003).

Association of obesity with anxiety, depression and emotional well‐being: a community survey. Australian and New Zealand Journal of Public Health, 27(4), 434-440.

Khor, G. L., Cobiac, L., & Skrzypiec, G. (2002). Gender differences in eating behavior and social selfconcept among Malaysian university students. Malaysian Journal of Nutrition, 8(1), 75-98.

Lan, S., Zhang, H., Zhong, R. Y., & Huang, G. Q. (2016). A customer satisfaction evaluation model forlogistics services using fuzzy analytic hierarchy process. Industrial Management & Data Systems.

Lum, M. (2018). Malaysia is Asia’s Fattest Country. The Star Malaysia.

Mahad, N. F., Yusof, N., & Ismail, N. F. (2019). The application of fuzzy analytic hierarchy process(FAHP) approach to solve multi-criteria decision making (MCDM) problems. Journal of Physics,1358, 1(012081).

Mahat, N., & Ahmad, S. (2017). Selection of the Best Thermal Massage Treatment for Diabetes byusing Fuzzy Analytical Hierarchy Process. Computing Research & Innovation (CRINN), 2, 34.

Mejia, N. (2016). Three Essays on Obesity: Food Environment, Attitudes Toward Food, and Cash Transfers (Doctoral dissertation, RAND).

Nazari, S., Fallah, M., Kazemipoor, H., & Salehipour, A. (2018). A fuzzy inference-fuzzy analytichierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Systems with Applications, 95, 261-271.

Nguyen, A. T., Nguyen, L. D., Le-Hoai, L., & Dang, C. N. (2015). Quantifying the complexity oftransportation projects using the fuzzy analytic hierarchy process. International journal of projectmanagement, 33(6), 1364-1376.

Pagani, L. S., Fitzpatrick, C., Barnett, T. A., & Dubow, E. (2010). Prospective associations betweenearly childhood television exposure and academic, psychosocial, and physical well-being by middlechildhood. Archives of pediatrics & adolescent medicine, 164(5), 425-431.

Rajappan, R., Selvaganapathy, K., & Liew, L. (2015). Physical activity level among university students:a cross-sectional survey. International Journal of Physiotherapy and Research, 3(6), 1336-1343.

Sturm, R., & Hattori, A. (2015). Diet and obesity in Los Angeles County 2007–2012: Is there ameasurable effect of the 2008 “Fast-Food Ban”? Social science & medicine, 133, 205-211.

World Health Organization (2019). Overweight and obesity (2019).https://www.who.int/gho/ncd/risk_factors/overweight/en/

Ying, Y.C., Kuang, K.L. & Ahmad, N.A. (2017). Physical activity and overweight/obesity among Malaysian adults: findings from the 2015 National Health and morbidity survey (NHMS). BMC Public Health, 17, 733.

Downloads

Published

2025-08-04