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ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION

Year 2011, Volume: 24 Issue: 4, 829 - 839, 10.02.2011

Abstract

The End Stage Renal Disease (ESRD) as a chronical health problem requires an expensive and a lifetime treatment called hemodialysis. It is important to obtain new information in order to reduce the cost of the treatment and to improve the quality of patient’s life. Treatment period requires a lot of clinical tests related to the risk factors for monitoring patient’s health and effectiveness of the treatment. These factors vary depending on demographic parameters such as age, gender, race, clinical parameters such as hematocrit level, albumine level, and also dialysis treatment prescription. In this paper, a data mining application including data preprocessing, data transformation, data mining algorithms and interpretation is used to find out patterns of risk factors as decision rules according to risk levels for dialysis patients in Turkey. A data set is formed by collecting 76 parameters of 170 patients on dialysis for 12 or more months at a dialysis center. CMS HCC (the Centers for Medicare and Medicaid Services -- Hierarchical Coexisting Conditions) ESRD model which includes relative coefficients of age, gender and comorbid diseases as scoring parameters is employed on data set in order to calculate risk scores for each patient and these scores are added to data set as a parameter called “Risk Score”. ESTARD and WEKA softwares are used in order to achieve classification, clustering and decision tree algorithms. Decision rules as results of application are interpreted with domain expert for medical significance.

 Keywords : Hemodialysis, Risk level, Risk score, Data Mining.

 

References

  • Internet: DIADER Report, “The Role of Private Dialysis Centers on Hemodialysis Service in Turkey, Relation of Quality-Cost, Costs and Paybacks of Dialysis, Reducing State Costs”, http://www.diader.org.tr/dosya/DiyalizRaporEkim20 09.pdf, October (2009). [2] Internet: http://www.nlm.nih.gov/medlineplus/ency/article/00 0500.htm M.D. Parul Patel, End-stage kidney disease December 8 (2009). [3] Internet: End Stage Renal Disease, http://www.healthsystem.virginia.edu/UVAHealth/ad ult_urology/endstage.cfm September 18 (2007).
  • Mries, M.F., “Modeling of hemodialysis patient hemoglobin: a data mining exploration” Master Thesis, The University of Iowa, (2007).
  • Bellazzi, R., Zupan, B., “Predictive data mining in clinical medicine: Current issues and guidelines”, International Journal of Medical Informatics, 77, 81-97 (2008).
  • Kusiak, A., Dixon, B., Shah S., “Predicting Survival Time for Kidney Dialysis Patients: A Data Mining Approach”, Computers in Biology and Medicine 35, 311–327 (2005).
  • Bellazzi, R., Larizza, C., Magni, P., Bellazzi, R., Temporal data mining for the quality assessment of hemodialysis services, Artificial Intelligence in Medicine, 34 25-39 (2005).
  • Knorr, T., Schmidt-Thieme, L., Johner, C., Identifying Patients at Risk: Mining Dialysis Treatment Data in: Okada, A., Imaizumi, T., Bock, H.H., Goal, W., eds., Cooperation in Classification and Data Analysis, Springer Berlin Heidelberg, 131- 140, (2009).
  • Daugirdas, J.T., Evanstone, J.C., Physiological principles and urea kinetic modeling, in: Daugirdas, J.T., Ing, T.S., eds. Handbook of Dialysis, Boston, Little Brown 15-45, (2000).
  • Basile, C., Casino, F., Lopez, T., “Percent reduction in blood urea concentration during dialysis estimates Kt/V in a simple and accurate way”, American Journal of Kidney Diseases, 15,40-45 (1990).
  • Internet: Implementation of Changes in End Stage Renal Disease (ESRD) Payment for Calendar Year 2006, http://www.cms.gov/MLNMattersArticles/downloads /MM4196.pdf, (2006).
  • Buntin, M.B., Garber, A.M., McClellan, M., Newhouse, J.P., “The Costs of Decedents in the Medicare Program: Implications for Payments to Medicare+Choice Plans”, Health Services Research, 39(1), 111–130 (2004).
  • Levy, J.M., Robst, J., Ingber, M.J., “Risk-Adjustment System for the Medicare Capitated ESRD Program”, Health Care Financing Review 27(4), 53-69 (2006).
  • Pope, G.C., Kautter, J., Ellis, R.P., Ash, A.S., Ayanian, J.Z., Iezzoni, L.I., Ingber, M.J., Levy, J.M., Robst, J., “Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model”, Health Care Financing Review, 25(4), 119-141 (2004).
  • Internet: Announcement of Calendar Year (CY) 2008 Medicare Advantage Capitation Rates and Payment Policies, http://www.cms.hhs.gov/MedicareAdvtgSpecRateStat s/Downloads/Announcement2010.pdf April 2nd, (2007).
  • Factor, K.F., “Potassium Management in Pediatric Peritoneal Dialysis Patients: Can a Diet With Increased Potassium Maintain a Normal Serum Potassium Without a Potassium Supplement?”, Advances in Peritoneal Dialysis, 23, 167-169 (2007).
  • Korevaar, J.C., van Manen, J.G., Dekker, F.W., de Waart, D.R., Boeschoten, E.W., Krediet, R.T., “Effect of an Increase in C-Reactive Protein Level during a Hemodialysis Session on Mortality”, Journal of the American Society of Nephrology, 15(11), 2916-2922 (2004).
  • Evrenkaya, T.R., Atasoyu, E.M., Ünver, S., Gültepe, M., Narin, Y., Tülbek, M.Y., The relationship between hemodialysis adequacy and co-morbid factors, Transplantation Journal 2(1), 44 (2002).
  • Obermayr, R.P., Temml, C., Gutjahr, G., Knechtelsdorfer, M., Oberbauer, R., Klauser-Braun, R., “Elevated Uric Acid Increases the Risk for Kidney Disease”, Journal of the American Society of Nephrology, 19(12), 2407–2413 (2008).
Year 2011, Volume: 24 Issue: 4, 829 - 839, 10.02.2011

Abstract

References

  • Internet: DIADER Report, “The Role of Private Dialysis Centers on Hemodialysis Service in Turkey, Relation of Quality-Cost, Costs and Paybacks of Dialysis, Reducing State Costs”, http://www.diader.org.tr/dosya/DiyalizRaporEkim20 09.pdf, October (2009). [2] Internet: http://www.nlm.nih.gov/medlineplus/ency/article/00 0500.htm M.D. Parul Patel, End-stage kidney disease December 8 (2009). [3] Internet: End Stage Renal Disease, http://www.healthsystem.virginia.edu/UVAHealth/ad ult_urology/endstage.cfm September 18 (2007).
  • Mries, M.F., “Modeling of hemodialysis patient hemoglobin: a data mining exploration” Master Thesis, The University of Iowa, (2007).
  • Bellazzi, R., Zupan, B., “Predictive data mining in clinical medicine: Current issues and guidelines”, International Journal of Medical Informatics, 77, 81-97 (2008).
  • Kusiak, A., Dixon, B., Shah S., “Predicting Survival Time for Kidney Dialysis Patients: A Data Mining Approach”, Computers in Biology and Medicine 35, 311–327 (2005).
  • Bellazzi, R., Larizza, C., Magni, P., Bellazzi, R., Temporal data mining for the quality assessment of hemodialysis services, Artificial Intelligence in Medicine, 34 25-39 (2005).
  • Knorr, T., Schmidt-Thieme, L., Johner, C., Identifying Patients at Risk: Mining Dialysis Treatment Data in: Okada, A., Imaizumi, T., Bock, H.H., Goal, W., eds., Cooperation in Classification and Data Analysis, Springer Berlin Heidelberg, 131- 140, (2009).
  • Daugirdas, J.T., Evanstone, J.C., Physiological principles and urea kinetic modeling, in: Daugirdas, J.T., Ing, T.S., eds. Handbook of Dialysis, Boston, Little Brown 15-45, (2000).
  • Basile, C., Casino, F., Lopez, T., “Percent reduction in blood urea concentration during dialysis estimates Kt/V in a simple and accurate way”, American Journal of Kidney Diseases, 15,40-45 (1990).
  • Internet: Implementation of Changes in End Stage Renal Disease (ESRD) Payment for Calendar Year 2006, http://www.cms.gov/MLNMattersArticles/downloads /MM4196.pdf, (2006).
  • Buntin, M.B., Garber, A.M., McClellan, M., Newhouse, J.P., “The Costs of Decedents in the Medicare Program: Implications for Payments to Medicare+Choice Plans”, Health Services Research, 39(1), 111–130 (2004).
  • Levy, J.M., Robst, J., Ingber, M.J., “Risk-Adjustment System for the Medicare Capitated ESRD Program”, Health Care Financing Review 27(4), 53-69 (2006).
  • Pope, G.C., Kautter, J., Ellis, R.P., Ash, A.S., Ayanian, J.Z., Iezzoni, L.I., Ingber, M.J., Levy, J.M., Robst, J., “Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model”, Health Care Financing Review, 25(4), 119-141 (2004).
  • Internet: Announcement of Calendar Year (CY) 2008 Medicare Advantage Capitation Rates and Payment Policies, http://www.cms.hhs.gov/MedicareAdvtgSpecRateStat s/Downloads/Announcement2010.pdf April 2nd, (2007).
  • Factor, K.F., “Potassium Management in Pediatric Peritoneal Dialysis Patients: Can a Diet With Increased Potassium Maintain a Normal Serum Potassium Without a Potassium Supplement?”, Advances in Peritoneal Dialysis, 23, 167-169 (2007).
  • Korevaar, J.C., van Manen, J.G., Dekker, F.W., de Waart, D.R., Boeschoten, E.W., Krediet, R.T., “Effect of an Increase in C-Reactive Protein Level during a Hemodialysis Session on Mortality”, Journal of the American Society of Nephrology, 15(11), 2916-2922 (2004).
  • Evrenkaya, T.R., Atasoyu, E.M., Ünver, S., Gültepe, M., Narin, Y., Tülbek, M.Y., The relationship between hemodialysis adequacy and co-morbid factors, Transplantation Journal 2(1), 44 (2002).
  • Obermayr, R.P., Temml, C., Gutjahr, G., Knechtelsdorfer, M., Oberbauer, R., Klauser-Braun, R., “Elevated Uric Acid Increases the Risk for Kidney Disease”, Journal of the American Society of Nephrology, 19(12), 2407–2413 (2008).
There are 17 citations in total.

Details

Primary Language English
Journal Section Industrial Engineering
Authors

Yunus Altıntas This is me

Hadi Gokcen

Mahir Ulgu

Neslihan Demirel

Publication Date February 10, 2011
Published in Issue Year 2011 Volume: 24 Issue: 4

Cite

APA Altıntas, Y., Gokcen, H., Ulgu, M., Demirel, N. (2011). ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION. Gazi University Journal of Science, 24(4), 829-839.
AMA Altıntas Y, Gokcen H, Ulgu M, Demirel N. ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION. Gazi University Journal of Science. December 2011;24(4):829-839.
Chicago Altıntas, Yunus, Hadi Gokcen, Mahir Ulgu, and Neslihan Demirel. “ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION”. Gazi University Journal of Science 24, no. 4 (December 2011): 829-39.
EndNote Altıntas Y, Gokcen H, Ulgu M, Demirel N (December 1, 2011) ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION. Gazi University Journal of Science 24 4 829–839.
IEEE Y. Altıntas, H. Gokcen, M. Ulgu, and N. Demirel, “ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION”, Gazi University Journal of Science, vol. 24, no. 4, pp. 829–839, 2011.
ISNAD Altıntas, Yunus et al. “ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION”. Gazi University Journal of Science 24/4 (December 2011), 829-839.
JAMA Altıntas Y, Gokcen H, Ulgu M, Demirel N. ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION. Gazi University Journal of Science. 2011;24:829–839.
MLA Altıntas, Yunus et al. “ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION”. Gazi University Journal of Science, vol. 24, no. 4, 2011, pp. 829-3.
Vancouver Altıntas Y, Gokcen H, Ulgu M, Demirel N. ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION. Gazi University Journal of Science. 2011;24(4):829-3.