Early Detection of Cardiovascular Disease in Patients with Chronic Kidney Disease using Data Mining Techniques

  • Avijit Kumar Chaudhuri
  • Arkadip Ray
  • Anirban Das
  • Prasun Chakrabarti
  • Dilip K. Banerjee


 A constant obstacle for doctors is the high prevalence of cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Increasing efforts have been made to jointly treat patients with heart and kidney disease, as shown by an increasing number of basic research and clinical investigations concerning CVD in CKD. Typical risk factors for CVD are common in CKD, such as age, blood pressure (bp), hypertension (htn), and blood sugar (sg). Standard risk factors tend to be the major contributors to CVD in patients with mild to moderate CKD. However, in patients with advanced CKD, non-traditional CKD-specific risk factors (e.g. Potassium level in blood) are more prevalent than in the general population, contributing, in addition to traditional risk factors, to the high burden of CVD in CKD. However, in patients with CKD, CVD often remains underdiagnosed and undertreated. Nevertheless, CVD still remains under control and care in patients with CKD. Researchers in this paper aims to predict the probability of CVD from CKD by using various popular data mining techniques and definitively propose a decision tree and by using Random Forest analysis to test its specificity and sensitivity to achieve concrete results with sufficient precision.

Keywords: Chronic Kidney Disease (CKD), Cardio Vascular Disease (CVD), Glomerular Filtration Rate (GFR), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF).


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[1] World Health Organization, “Preventing Chronic Disease: A Vital Investment,” Geneva, WHO, 2005.
[2] Grassmann, A., Gioberge, S., Moeller, S., & Brown, G. (2005). ESRD patients in 2004: global overview of patient numbers, treatment modalities and associated trends. Nephrol. Dial. Transplant., 20(12), 2587-2593.
[3] Abdelhmid, S. M. S., & Ajith, A. (2014). Novel Ensemble Decision Support and Health Care Monitoring System. Journal of Network and Innovative Computing, 2, 041-051.
[4] Han, J., & Kamber, M. (2003). Data mining: concepts and techniques, 3rd ed. Burlington, MA, Elsevier.
[5] Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A Review on Ensembles for the Class Imbalance Problem: Bagging, Boosting and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
[6] Sen, A. K., Patel, S. B., & Shukla, D. D. (2013). A data mining technique for prediction of coronary heart disease using neuro-fuzzy integrated approach two level. International Journal of Engineering & Computer Science, 2(9), 2663-2671.
[7] Herland, M., Khoshgoftaar, T. M., & Wald, R. (2014). A review of data mining using big data in health informatics. Journal of Big Data, 1(1), 1.
[8] Masethe, H. D., & Masethe, M. A. (2014). Prediction of Heart Disease using Classification Algorithms. in Proc. of the World Congress on Engineering and Computer Science, 2, 22-24.
[9] Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., & Salehi, M. (2010, October). Predicting breast cancer survivability using data mining techniques. In 2010 2nd International Conference on Software Technology and Engineering (Vol. 2, pp. V2-227). IEEE.
[10] Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung Journal of Medical Sciences, 29(2), 93-99.
[11] Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E., & Hsu, C. Y. (2004). Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. New Engl J Med, 351(13), 1296-1305.
[12] Bright, R. (1836). Cases and observations illustrative of renal disease accompanied with the secretion of albuminous urine. Med. Chir. Rev., 25(49), 23-35.
[13] Gansevoort, R. T., Correa-Rotter, R., Hemmelgarn, B. R., Jafar, T. H., Heerspink, H. J. L., Mann, J. F., ... & Wen, C. P. (2013). Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet, 382(9889), 339-352.
[14] Eckardt, K. U., Coresh, J., Devuyst, O., Johnson, R. J., Köttgen, A., Levey, A. S., & Levin, A. (2013). Evolving importance of kidney disease: from subspecialty to global health burden. Lancet, 382(9887), 158-169.
[15] Eknoyan, G., Lameire, N., Eckardt, K., Kasiske, B., Wheeler, D., Levin, A., ... & Levey, A. S. (2013). KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int, 3(1), 5-14.
[16] Matsushita, K., van der Velde, M., Astor, B. C., Woodward, M., Levey, A. S., de Jong, P. E., ... & Chronic Kidney Disease Prognosis Consortium. (2010). Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet, 375(9731), 2073-2081.
[17] Gansevoort, R. T., Matsushita, K., Van Der Velde, M., Astor, B. C., Woodward, M., Levey, A. S., ... & Coresh, J. (2011). Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int, 80(1), 93-104.
[18] Wen, C. P., Cheng, T. Y. D., Tsai, M. K., Chang, Y. C., Chan, H. T., Tsai, S. P., ... & Wen, S. F. (2008). All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet, 371(9631), 2173-2182.
[19] Hemmelgarn, B. R., Clement, F., Manns, B. J., Klarenbach, S., James, M. T., Ravani, P., ... & Jindal, K. (2009). Overview of the Alberta kidney disease network. BMC Nephrol, 10(1), 30.
[20] Franco, O. H., Steyerberg, E. W., Hu, F. B., Mackenbach, J., & Nusselder, W. (2007). Associations of diabetes mellitus with total life expectancy and life expectancy with and without cardiovascular disease. Arch Intern Med, 167(11), 1145-1151.
[21] Franco, O. H., Peeters, A., Bonneux, L., & De Laet, C. (2005). Blood pressure in adulthood and life expectancy with cardiovascular disease in men and women: life course analysis. Hypertension, 46(2), 280-286.

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Chaudhuri, A. K., Ray, A., Das, A., Chakrabarti, P., & Banerjee, D. K. (2020). Early Detection of Cardiovascular Disease in Patients with Chronic Kidney Disease using Data Mining Techniques. Asian Journal For Convergence In Technology (AJCT), 6(3), 65-76. https://doi.org/10.33130/AJCT.2020v06i03.011