IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
Scopus/Elsevier
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Study On Correlating Factors That Affect The Health Status Among Obesity In Elderly: A Preliminary Study

[Full Text]

 

AUTHOR(S)

Wan Muhamad Amir W Ahmad, Farah Muna Mohamad Ghazali, Muhammad Azeem Yaqoob, Rabiatul Adawiyah Abdul Rohim, Ruhaya Hasan

 

KEYWORDS

Chi-Square Analysis (CA), Binary Logistic Regression (BLR) and Multilayer Perceptron (MLP), Gender, Elderlies, Duration of Stay, Health Status.

 

ABSTRACT

The aim of this paper is to examine all possible association among all the studied factor which influencing the health status among the elderly at Rumah Seri Kenangan (RSK), Pengkalan Chepa, Kelantan and RSK Bedong, Kedah. RSK is a government-funded public sheltered home for the elderly suffering from a lack of financial and family support. In this paper, four different methods have been applied in order to determine all the possible association which probably has a higher tendency towards the health status. Chi-square Analysis (CA), Binary Logistic Regression (BLR) and Multilayer Perceptron (MLP) were selected statistical tools for the factor determine as for the elderly at Rumah Seri Kenangan (RSK). The significant variable from CA will be used for the BLR analysis, for assessing the odd ratio. The next analysis is to conform (validate) the relationship between the dependent and independent variables. To achieve that the MLP procedure is applied. This methodology discovers the relationship between the target variable and the predictors for assessing health status among the elderlies. The obtained results from these analyses will be used as an indicator for improving the level of quality given to the elderly. In conclusion, there are two factors that contribute to the health status of the elderlies, gender factor, and duration of the stay at the RSK. The female elderly is more healthy compared to the male elderly. According to the duration of the stay, the elderly who had stayed more than 60 months is much healthier compared to the elderly who had to stay less than 60 months.

 

REFERENCES

[1] Rubenstein L., Calkins D., Greenfield S., Jette A., Meenan R.f., Nevins M. E. Al.(1989). Health status assessment for elderly patients. Report of the Society of General Internal Medicine Task Force on Health Assessment. J Am Geriatr Soc, 37:562–569.
[2] Honorato D.S.D.C.V., Rossato S.l., Fuchs F., Harzheim E., Fuchs S.(2013). Assessment of primary health care received by the elderly and health related quality of life: A crosssectional study. BMC Public Health, 13:605
[3] Stuck A., Walthert J., Nikolaus T, Büla C., Hohmann C., Beckjc. (1999) Risk factors for functional status decline in community living elderly people: A systematic literature review. Soc Sci Med., 48:445–469.
[4] Kinsella, K. (1996). Demograhic aspects. Ebrahim, S. Kalache, A. eds. Epidemiology in Old Age. Pp: 32-40. London British Medical Journal London, UK.
[5] Elia, M., 2001. Obesity in the elderly. Obesity research, 9(S11), pp.244S-248S.
[6] Visser M., Lanlois, J., Guralnik, J. M. Et al. (1998). High body fatness, but not low fat free mass, predicts disability in older men and women: the Cardiovascular Health Study. Am J Clin Nutr 68: 584-590.
[7] Alexopoulos, G. S. (1992). Geriatric depression reaches maturity. International Journal of Geriatric Psychiatry. 7, 305–306.
[8] DJS Research, (2019). Correlation Analysis. Market Research. https://www.djsresearch.co.uk/glossary/item/correlation-analysis-market-research [13 November 2019].
[9] Pizzi N.J., Somorjai R.L., Pedrycz W. (2006).Classifying Biomedical Spectra Using Stochastic Feature Selection and Parallelized Multi-Layer Perceptrons. Modern Information Processing. Vol. 4, Issue 2, 2006, pp. 383-393.
[10] Ripley B.D. (1994). Neural networks and related methods for classification. J. Royal Statistical Society [B], 56, pp. 409-456.
[11] Kejian C, Wenjuan L., Yuntong S., Zulin H., Min Tan, Xiaodong L., Li Gu and Yongzhi J (2018). Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large Scale Tidal Flat Reclamation. Water. Vol 10 (69). Pp: 1-18.