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1860796986035273728
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2024-10-04 15:42
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| format |
Restricted Document
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| id |
10937
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UniSZA
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| internalnotes |
A Rashid K, KA Narayan, Azizah Hj Ab Manan. The Prevalence of Hypertension among the Elderly in Fourteen Villages in Kedah, Malaysia. Malaysian Journal of Medicine and Health Sciences Vol. 4(2) June 2008:33-39. Brundtland GH. 2002. From the World Health Organization. Reducing risks to health, promoting healthy life. Jama. 288:1974. D. W. Hosmer and S. Lemeshow, Applied logistic regression, second edition, John Wiley and Sons, 2000. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ; Comparative Risk Assessment Collaborating Group. Selected major risk factors and global and regional burden of disease. Lancet 2002; 360: 1347-60. Haberman, S. (1978). Analysis of qualitative data (Vol. 1). New York: Academic Press. Jo I, Ahn Y, Lee J, Shin KR, Lee HK, Shin C. Prevalence, Awareness, Treatment, Control and Risk Factors of Hypertension in Korea: the Ansan Study. Journal of Hypertension 2001; 9:1523-1532. Joshi and Pooja V. 2010. Prevalence of hypertension in the U.S. adult population and its association with various behavioral risk factors. San Diego State University Library & Information Access. Kotsis V, Stabouli S, Bouldin M, Low A, Toumanidis S, Zakopoulos N. Impact of Obesity on 24-Hour Ambulatory Blood Pressure and Hypertension. Hypertension 2005; 45:602-607. Lawes CM, Rodgers A, Bennett DA, Parag V, Suh I, Ueshima H. Blood Pressure and Cardiovascular Disease in the Asia Pacific Region. Journal of Hypertension 2004; 21(4):707-16. N. N. Naing. 2003. Determination of sample size. Malaysian Journal of Medical. National Nutrition Monitoring Bureau, National Institute of Nutrition. Diet & nutritional status of population and prevalence of hypertension among adults in rural areas. NNMB Technical Report No. 24. Hyderabad: National Institute of Nutrition; 2006. Patricia K, Megan W, Kristi R, Paul K, Whelton P, Jiang H. Worldwide Prevalence of Hypertension: A Systematic Review. Journal of Hypertension 2004; 22(1):11-19. Primatesta P, Poulter NR. Improvement in Hypertension Management in England: Results from the Health Survey for England 2003. Journal of Hypertension 2006; 24(6):1187-92. Rankinen T, Church TS, Rice T, Bouchard C, Blair SN. Cardiorespiratory Fitness, BMI, and Risk of Hypertension: The HYPGENE Study. Medicine & Science in Sports and Exercise 2007; 39(10):1687-92. Schlesselman, J. J. (1982). Case control studies: Design, control, analysis. New York: Oxford University Press. Singh RB, Suh IL, Singh VP, Chaithiraphan S, Laothavorn P,Sy RG. Hypertension and Stroke in Asia: Prevalence, Control and Strategies in Developing Countries for Prevention. Journal of Human Hypertension 2010; 14(10/11), 749-763. S. K. Lwanga and S. Lemeshow, sample size determination in health studies: A practical manual. World Health Organization, 1991, 1-3. Tanji JL, Champlin JJ, Wong GY, Lew EY, Brown TC, Amsterdam EA. Blood pressure recovery curves after submaximal exercise: a predictor of hypertension at ten-year follow-up. Am J Hypertens. 1989; 2: 135–138. Thomas JW, Ramachandran SV. Epidemiology of Uncontrolled Hypertension in the United States. Circulation 2005; 112: 1651- 1662. Truett J, Cornfield J, Kannel W. 1967. A multivariate analysis of the risk of coronary heart disease in Framingham. J Chron Dis 20: 511–524. W. M. A. W. Ahmad, N. A. Aleng and Zalila Ali, binary logistic regression analysis technique used in analyzing the categorical data in education science: a case study of Terengganu state, Malaysia. World Appl. Sci. J.,9(9), 2010, 1062- 1066. W. M. A. W. Ahmad, N. A. Aleng, Zalila Ali and Arif Bin Awang Nawi, 2011. modelling associated factors of hiv-infected tuberculosis (tb) patients using path model analysis. World Appl. Sci. J., 12(9), 2011, 1580-1584.
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oai_dc
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10937 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10937 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 629 96 96 1341 2024-10-04 15:42 1341x629 5082-01-FH02-FIK-14-00735.jpg UniSZA Private Access Association of hypertension with risk factors using logistic regression Applied Mathematical Sciences Hypertension is one of the important public health challenges worldwide because of its high frequency and concomitant risks of cardiovascular and kidney disease. A hypertension model is built to test the interaction and significance between the factors. In this present paper, we present the results that gained from multiple logistic regression method and used to model the relationship between the ordinal outcome variable. The significant variables is chosen based on the p-value associated to the significant level of model that lies on α = 0.05. Logit determination and the correlation between the variables are also discussed for further analysis. there are three factors that most significant of the six factors tested were identified as having influence significantly the performance of human blood pressure (hypertension). These factors are age (p-value <0.000), body mass index (p-value <0.001) and systolic (p-value <0.001). The use of mathematical software PASW version 18 is applied in this research as an alternatives calculation procedures derived from the methodology. 8 49 2563-2572 A Rashid K, KA Narayan, Azizah Hj Ab Manan. The Prevalence of Hypertension among the Elderly in Fourteen Villages in Kedah, Malaysia. Malaysian Journal of Medicine and Health Sciences Vol. 4(2) June 2008:33-39. Brundtland GH. 2002. From the World Health Organization. Reducing risks to health, promoting healthy life. Jama. 288:1974. D. W. Hosmer and S. Lemeshow, Applied logistic regression, second edition, John Wiley and Sons, 2000. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ; Comparative Risk Assessment Collaborating Group. Selected major risk factors and global and regional burden of disease. Lancet 2002; 360: 1347-60. Haberman, S. (1978). Analysis of qualitative data (Vol. 1). New York: Academic Press. Jo I, Ahn Y, Lee J, Shin KR, Lee HK, Shin C. Prevalence, Awareness, Treatment, Control and Risk Factors of Hypertension in Korea: the Ansan Study. Journal of Hypertension 2001; 9:1523-1532. Joshi and Pooja V. 2010. Prevalence of hypertension in the U.S. adult population and its association with various behavioral risk factors. San Diego State University Library & Information Access. Kotsis V, Stabouli S, Bouldin M, Low A, Toumanidis S, Zakopoulos N. Impact of Obesity on 24-Hour Ambulatory Blood Pressure and Hypertension. Hypertension 2005; 45:602-607. Lawes CM, Rodgers A, Bennett DA, Parag V, Suh I, Ueshima H. Blood Pressure and Cardiovascular Disease in the Asia Pacific Region. Journal of Hypertension 2004; 21(4):707-16. N. N. Naing. 2003. Determination of sample size. Malaysian Journal of Medical. National Nutrition Monitoring Bureau, National Institute of Nutrition. Diet & nutritional status of population and prevalence of hypertension among adults in rural areas. NNMB Technical Report No. 24. Hyderabad: National Institute of Nutrition; 2006. Patricia K, Megan W, Kristi R, Paul K, Whelton P, Jiang H. Worldwide Prevalence of Hypertension: A Systematic Review. Journal of Hypertension 2004; 22(1):11-19. Primatesta P, Poulter NR. Improvement in Hypertension Management in England: Results from the Health Survey for England 2003. Journal of Hypertension 2006; 24(6):1187-92. Rankinen T, Church TS, Rice T, Bouchard C, Blair SN. Cardiorespiratory Fitness, BMI, and Risk of Hypertension: The HYPGENE Study. Medicine & Science in Sports and Exercise 2007; 39(10):1687-92. Schlesselman, J. J. (1982). Case control studies: Design, control, analysis. New York: Oxford University Press. Singh RB, Suh IL, Singh VP, Chaithiraphan S, Laothavorn P,Sy RG. Hypertension and Stroke in Asia: Prevalence, Control and Strategies in Developing Countries for Prevention. Journal of Human Hypertension 2010; 14(10/11), 749-763. S. K. Lwanga and S. Lemeshow, sample size determination in health studies: A practical manual. World Health Organization, 1991, 1-3. Tanji JL, Champlin JJ, Wong GY, Lew EY, Brown TC, Amsterdam EA. Blood pressure recovery curves after submaximal exercise: a predictor of hypertension at ten-year follow-up. Am J Hypertens. 1989; 2: 135–138. Thomas JW, Ramachandran SV. Epidemiology of Uncontrolled Hypertension in the United States. Circulation 2005; 112: 1651- 1662. Truett J, Cornfield J, Kannel W. 1967. A multivariate analysis of the risk of coronary heart disease in Framingham. J Chron Dis 20: 511–524. W. M. A. W. Ahmad, N. A. Aleng and Zalila Ali, binary logistic regression analysis technique used in analyzing the categorical data in education science: a case study of Terengganu state, Malaysia. World Appl. Sci. J.,9(9), 2010, 1062- 1066. W. M. A. W. Ahmad, N. A. Aleng, Zalila Ali and Arif Bin Awang Nawi, 2011. modelling associated factors of hiv-infected tuberculosis (tb) patients using path model analysis. World Appl. Sci. J., 12(9), 2011, 1580-1584.
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| spellingShingle |
Association of hypertension with risk factors using logistic regression
|
| summary |
Hypertension is one of the important public health challenges worldwide because of its high frequency and concomitant risks of cardiovascular and kidney disease. A hypertension model is built to test the interaction and significance between the factors. In this present paper, we present the results that gained from multiple logistic regression method and used to model the relationship between the ordinal outcome variable. The significant variables is chosen based on the p-value associated to the significant level of model that lies on α = 0.05. Logit determination and the correlation between the variables are also discussed for further analysis. there are three factors that most significant of the six factors tested were identified as having influence significantly the performance of human blood pressure (hypertension). These factors are age (p-value <0.000), body mass index (p-value <0.001) and systolic (p-value <0.001). The use of mathematical software PASW version 18 is applied in this research as an alternatives calculation procedures derived from the methodology.
|
| title |
Association of hypertension with risk factors using logistic regression
|
| title_full |
Association of hypertension with risk factors using logistic regression
|
| title_fullStr |
Association of hypertension with risk factors using logistic regression
|
| title_full_unstemmed |
Association of hypertension with risk factors using logistic regression
|
| title_short |
Association of hypertension with risk factors using logistic regression
|
| title_sort |
association of hypertension with risk factors using logistic regression
|