Chronic Kidney Disease Risk Estimation Using Artificial Neural Network
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Abstract
Chronic kidney disease (CKD) is the most common disease of the urinary system that can threaten the survival of the human body. Early detection and lifestyle changes can prevent kidney failure and improve the chance of survival. In West Malaysia, the prevalence of chronic kidney disease is estimated to be 9% of the population. However, screening for chronic kidney disease is still neglected at the early stages. Many equations for risk estimation of kidney failure have been developed. Some of the limitations of these equations are that they may require many laboratory tests, static and not updated. In this study, a new risk estimation model for kidney disease is developed. The risk factors of kidney disease are first identified according to their energy levels, which are Low, Medium and High. The new equation is then developed based on the relationship and the estimated weight of these risk factors.
Artificial Neural Network (ANN) is utilized in this study as an alternative to classic risk equations. The MATLAB software is used to train the neural network. Retrospective data from 20 subjects are used to compare the output for the conventional equation and ANN. Another 20 samples have also been generated and compared with “Kidney Disease: Improving Global Outcomes” (KDIGO) 2012 clinical guideline heat map. The results show a slight difference between the methods. The conventional method shows its capability to estimate the risk. The result also shows the potential of the artificial neural network (ANN) to improve the accuracy of chronic kidney disease risk estimation.
Manuscript received: 3 May 2019 | Revised: 10 Sept 2019 | Accepted: 14 Oct 2019 | Published: 13 Nov 2019
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References
US Department of Health and Human Services, Center for Disease Control and Prevention, “National Chronic Kidney Disease Fact Sheet 2017,” stacks.cdc.gov.
URL: https://stacks.cdc.gov/view/cdc/46034/cdc_46034_DS1.pdf
(accessed 20 Dec, 2018)
N. Tangri, L. A. Stevens, J. Griffith, H. Tighiouart, O. Djurdjev, D. Naimark and A. S. Levey, “A predictive model for progression of chronic kidney disease to kidney failure,” JAMA: The Journal of the American Medical Association, vol. 305, no. 15, pp. 1553–1559, 2011.
DOI: https://doi.org/10.1001/jama.2011.451
A. S. Levey and L. A. & Stevens, “Estimating GFR Using the CKD Epidemiology Collaboration (CKD-EPI) Creatinine Equation: More Accurate GFR Estimates, Lower CKD Prevalence Estimates, and Better Risk Predictions,” American Journal of Kidney Diseases, vol. 55, no. 4, pp. 622–627, 2010.
DOI: https://doi.org/10.1053/j.ajkd.2010.02.337
L. A. Inker, J. Eckfeldt, A. S. Levey, C. Leiendecker-Foster, G. Rynders, J. Manzi and J. Coresh, “Expressing the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) cystatin C equations for estimating GFR with standardized serum cystatin C Values,” American Journal of Kidney Diseases, vol. 58, no. 4, pp. 682-684, 2011.
DOI: https://doi.org/10.1053/j.ajkd.2011.05.019
L. A. Stevens, J. Manzi, A. S. Levey, J. Chen, A. E. Deysher, T. Greene, E. D. Poggio, C. H. Schmid, M. W. Steffes, Y. L. Zhang and F. Van Lente, “Impact of Creatinine Calibration on Performance of GFR Estimating Equations in a Pooled Individual Patient Database,” American Journal of Kidney Diseases, vol. 50, no. 1, pp. 21–35, 2007.
DOI: https://doi.org/10.1053/j.ajkd.2007.04.004
L. M. Pang, K. M. Tay and C. P. Lim, “Monotone fuzzy rule relabeling for the zero-order TSK fuzzy inference system”, IEEE Transactions on Fuzzy Systems, vol. 24, no. 6, pp. 1455-1463, 2016.
DOI: https://doi.org/10.1109/TFUZZ.2016.2540059
Y. W. Kerk, K. M. Tay and C. P. Lim, “An analytical interval fuzzy inference system for risk evaluation and prioritization in failure mode and effect analysis,” IEEE Systems Journal, vol. 11, no. 3, pp. 1589-600, 2017.
DOI: https://doi.org/10.1109/JSYST.2015.2478150
P. S. Patra, D. P. Sahu and I. Mandal, “An expert system for diagnosis of human diseases,” International Journal of Computer Applications, vol. 1, no. 13, pp. 71-73, 2010.
DOI: https://doi.org/10.1016/B978-0-12-821777-1.00022-7
D. Pal, K. M. Mandana, S. Pal, D. Sarkar and C. Chakraborty, “Fuzzy expert system approach for coronary artery disease screening using clinical parameters,” Knowledge-Based Systems, vol. 36, pp.162-174, 2012.
DOI: https://doi.org/10.1016/j.knosys.2012.06.013
N. Allahverdi, S. Torun and I. Saritas, “Design of a fuzzy expert system for determination of coronary heart disease risk,” ACM Proceedings of the 2007 International Conference on Computer Systems and Technologies, pp. 36, 2007.
DOI: https://doi.org/10.1145/1330598.1330638
L. Á. Menéndez, F. J. de Cos Juez, F. S. Lasheras and J. Á. Riesgo, “Artificial neural networks applied to cancer detection in a breast screening programme,” Mathematical and Computer Modelling, vol. 52, no. 7-8, pp. 983-991, 2010.
DOI: https://doi.org/10.1016/j.mcm.2010.03.019
M. R. Pooja and M. P. Pushpalatha, “A Neural Network Approach for Risk Assessment of Asthma Disease,” Journal of Health Informatics & Management, vol. 2, no. 1, 2018.
DOI: https://doi.org/10.4172/JHIM.1000109
S. Agrawal and J. Agrawal, “Neural network techniques for cancer prediction: A survey,” Procedia Computer Science, vol. 60, pp. 769-774, 2015.
DOI: https://doi.org/10.1016/j.procs.2015.08.234
M. Catalogna, E. Cohen, S. Fishman, Z. Halpern, U. Nevo and E. Ben-Jacob, “Artificial neural networks based controller for glucose monitoring during clamp test,” PLoS ONE, vol. 7, no. 8, pp. e44587, 2012.
DOI: https://doi.org/10.1371/journal.pone.0044587
E. Elveren and N. Yumusak, “Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm,” Journal of Medical Systems, vol. 35, no. 3, pp. 329-332, 2011.
DOI: https://doi.org/10.1007/s10916-009-9369-3
D. C. Barbosa, D. B. Roupar, J. C. Ramos, A. C. Tavares and C. S. Lima, “Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images,” Biomedical Engineering Online, vol. 11, no. 1, pp. 3, 2012.
DOI: https://doi.org/10.1186/1475-925X-11-3
Y. Li, A. M. Rauth and X. Y. Wu, “Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks,” European Journal of Pharmaceutical Sciences, vol. 24, no. 5, pp. 401-410, 2005.
DOI: https://doi.org/10.1016/j.ejps.2004.12.005
J. Coresh, E. Selvin, L. A. Stevens, J. Manzi, J. W. Kusek, P. Eggers, F. Van Lente and A. S. Levey, “Prevalence of chronic kidney disease in the United States,” JAMA, vol. 298, no. 17, pp. 2038–2047, 2007.
DOI: https://doi.org/10.1001/jama.298.17.2038
G. Suleymanlar, C. Utas, T. Arinsoy, K. Ates, B. Altun, M. R. Altiparmak, T. Ecder, M. E. Yilmaz, T. Camsari, A. Basci and K. Serdengecti, “A population-based survey of Chronic Renal Disease In Turkey—the CREDIT study,” Nephrology Dialysis Transplantation, vol. 26, no. 6, pp. 1862-1871, 2010.
DOI: https://doi.org/10.1093/ndt/gfq656
I. Goldberg and I. Krause, “The role of gender in chronic kidney disease,” European Medical Journal, vol. 1, no. 2, pp. 58-64, 2016.
DOI: https://doi.org/10.33590/emj/10312319
A. S. Levey, L. A. Stevens, C. H. Schmid, Y. L. Zhang, A. F. Castro, H. I. Feldman, J. W. Kusek, P. Eggers, F. Van Lente, T. Greene and J. Coresh, “A new equation to estimate glomerular filtration rate,” Annals of Internal Medicine, vol. 150, no. 9, pp. 604-612, 2009.
DOI: https://doi.org/10.7326/0003-4819-150-9-200905050-00006
United States Renal Data System, “2015 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States,” usrds.org.
URL: http://www.usrds.org/adr.aspx (accessed 20 Dec, 2018).
K. B. Werner, S. Elmstahl, A. Christensson and M. Pihlsgård, “Male sex and vascular risk factors affect cystatin C-derived renal function in older people without diabetes or overt vascular disease,” Age and Ageing, vol. 43, no. 3, pp. 411-417, 2013.
DOI: https://doi.org/10.1093/ageing/aft191
D. Nitsch, D. F. Dietrich, A. von Eckardstein, J. M. Gaspoz, S. H. Downs, P. Leuenberger, J. M. Tschopp, O. Brändli, R. Keller, M. W. Gerbase and N. M. Probst-Hensch, “Prevalence of renal impairment and its association with cardiovascular risk factors in a general population: results of the Swiss SAPALDIA study,” Nephrology Dialysis Transplantation, vol. 21, no. 4, pp. 935-944, 2006.
DOI: https://doi.org/10.1093/ndt/gfk021
W. W. Brown, R. M. Peters, S. E. Ohmit, W. F. Keane, A. Collins, S. C. Chen, K. King, M. J. Klag, D. A. Molony and J. M. Flack, “Early detection of kidney disease in community settings: the Kidney Early Evaluation Program (KEEP),” American Journal of Kidney Diseases, vol. 42, no. 1, pp. 22-35, 2003.
DOI: https://doi.org/10.1016/S0272-6386(03)00405-0
S. J. Chadban, E. M. Briganti, P. G. Kerr, D. W. Dunstan, T. A. Welborn, P. Z. Zimmet and R. C. Atkins, “Prevalence of kidney damage in Australian adults: The AusDiab kidney study,” Journal of the American Society of Nephrology, vol. 14, no. suppl 2, pp. S131-S138, 2003.
DOI: https://doi.org/10.1097/01.ASN.0000070152.11927.4A
M. E. Tarver-Carr, N. R. Powe, M. S. Eberhardt, T. A. LaVeist, R. S. Kington, J. Coresh and F. L. Brancati, “Excess risk of chronic kidney disease among African-American versus white subjects in the United States: a population-based study of potential explanatory factors,” Journal of the American Society of Nephrology, vol. 13, no. 9, pp. 2363-2370, 2002.
DOI: https://doi.org/10.1097/01.ASN.0000026493.18542.6A
P. Muntner, B. Newsome, H. Kramer, C. A. Peralta, Y. Kim, D. R. Jacobs, C. I Kiefe and C. E. Lewis, “Racial differences in the incidence of chronic kidney disease,” Clinical Journal of the American Society of Nephrology, vol. 7, no. 1, pp. 101-107, 2012.
DOI: https://doi.org/10.2215/CJN.06450611
E. Y. Song, W. M. McClellan, A. McClellan, R. Gadi, A. C. Hadley, J. Krisher, M. Clay and B. I. Freedman, “Effect of community characteristics on familial clustering of end-stage renal disease,” American Journal of Nephrology, vol. 30, no. 6, pp. 499-504, 2009.
DOI: https://doi.org/10.1159/000243716
W. M. McClellan, D. G. Warnock, S. Judd, P. Muntner, R. E. Patzer, B. D. Bradbury, L. A. McClure, B. B. Newsome and G. Howard, “Association of family history of ESRD, prevalent albuminuria, and reduced GFR with incident ESRD,” American Journal of Kidney Diseases, vol. 59, no. 1, pp. 25-31, 2012.
DOI: https://doi.org/10.1053/j.ajkd.2011.09.018
K. Matsushita, B. K. Mahmoodi, M. Woodward, J. R. Emberson, T. H. Jafar, S. H. Jee, K. R. Polkinghorne, A. Shankar, D. H. Smith, M. Tonelli and D. G. Warnock DG, “Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate,” JAMA, vol. 307, no. 18, pp. 1941-1951, 2012.
DOI: https://doi.org/10.1001/jama.2012.3954
L. A. Inker, B. C. Astor, C. H. Fox, et al., “KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD,” American journal of kidney diseases: the official journal of the National Kidney Foundation, vol. 63, no. 5, pp. 713-755, 2014.
DOI: https://doi.org/10.1053/j.ajkd.2014.01.416
A. Chang and H. Kramer, “Should eGFR and Albuminuria Be Added to the Framingham Risk Score Chronic Kidney Disease and Cardiovascular Disease Risk Prediction,” Nephron Clinical Practice, vol. 119, no. 2, pp. c171-c178, 2011.
DOI: https://doi.org/10.1159/000325669
F. W. Spierto, W. H. Hannon, E. W. Gunter and S. J. Smith, “Stability of urine creatinine,” Clinica Chimica Acta, vol. 264, no. 2, pp. 227-232, 1997.
DOI: https://doi.org/10.1016/S0009-8981(97)00080-6
R. G. Nelson and K. R. Tuttle, “The new KDOQITM clinical practice guidelines and clinical practice recommendations for diabetes and CKD,” Blood Purification, vol. 25, no. 1, pp. 112-114, 2007.
DOI: https://doi.org/10.1159/000096407
A. Chockalingam, N. R. Campbell and J. G. Fodor, “Worldwide epidemic of hypertension,” Canadian Journal of Cardiology, vol. 22, no. 7, pp. 553-555, 2006.
DOI: https://doi.org/10.1016/s0828-282x(06)70275-6
M. V. Rao, Y. Qiu, C. Wang and G. Bakris, “Hypertension and CKD: Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES), 1999-2004,” American Journal of Kidney Diseases, vol. 51, no. 4, pp. S30-S37, 2008.
DOI: https://doi.org/10.1053/j.ajkd.2007.12.012
J. M. Sontrop, S. N. Dixon, A. X. Garg, I. Buendia-Jimenez, O. Dohein, S. H. Huang and W. F. Clark, “Association between water intake, chronic kidney disease, and cardiovascular disease: a cross-sectional analysis of NHANES data,” American Journal of Nephrology, vol. 37, no. 5, pp. 434-442, 2013.
DOI: https://doi.org/10.1159/000350377
R. Yacoub, H. Habib, A. Lahdo, R. Al Ali, L. Varjabedian, G. Atalla, N. K. Akl, S. Aldakheel, S. Alahdab and S. Albitar, “Association between smoking and chronic kidney disease: a case control study,” BMC Public Health, vol. 10, no. 1, pp. 731, 2010.
DOI: https://doi.org/10.1186/1471-2458-10-731
S. L. Rodrigues, P. R. Souza Júnior, E. B. Pimentel, M. P. Baldo, D. C. Malta, J. G. Mill and C. L. Szwarcwald, “Relationship between salt consumption measured by 24-h urine collection and blood pressure in the adult population of Vitória (Brazil),” Brazilian Journal of Medical and Biological Research, vol. 48, no. 8, pp. 728-735, 2015.
DOI: https://doi.org/10.1590/1414-431x20154455
C. Crump, J. Sundquist, M. A. Winkleby and K. Sundquist, “Low stress resilience in late adolescence and risk of hypertension in adulthood,” Heart, vol. 102, no. 7, pp. 541-547, 2016.
DOI: https://doi.org/10.1136/heartjnl-2015-308597
K. M. Diaz and D. Shimbo, “Physical activity and the prevention of hypertension,” Current Hypertension Reports, vol. 15, no. 6, pp. 659-668, 2013.