Exploring the role of urine analysis in early detection of chronic kidney disease
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Published:
Jun 30, 2023
Keywords:
Chronic Kidney Disease, Early Detection, Diagnostic Model, Performance Evaluation, Clinical Significance
Section
Basic and Applied Mathematics Science
Statistics Article
Article View : 44 Times
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Abstract
This study focuses on the development and validation of a urine-based diagnostic model for early detection of Chronic Kidney Disease (CKD). Only urine indicators, hypertension, diabetes mellitus, coronary artery disease, appetite, pedal edema, and anemia, are selected based on their relevance to CKD. A decision tree algorithm is utilized for model development, with specific parameters set for optimal performance. The model is trained and evaluated using two datasets, demonstrating promising results in terms of 100% true positive and true negative rates in validation study. The findings highlight the potential clinical significance and applicability of the developed model for timely interventions in CKD patients.
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How to Cite
H., F. (2023). Exploring the role of urine analysis in early detection of chronic kidney disease . International Journal of Basic and Applied Science, 12(1), 33–38. https://doi.org/10.35335/ijobas.v12i1.248
References
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N. R. Hill et al., “Global prevalence of chronic kidney disease–a systematic review and meta-analysis,” PLoS One, vol. 11, no. 7, p. e0158765, 2016.
V. Jha et al., “Chronic kidney disease: global dimension and perspectives,” Lancet, vol. 382, no. 9888, pp. 260–272, 2013.
S. Herget-Rosenthal, “Imaging techniques in the management of chronic kidney disease: current developments and future perspectives,” in Seminars in nephrology, 2011, vol. 31, no. 3, pp. 283–290.
R. T. Gansevoort et al., “Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention,” Lancet, vol. 382, no. 9889, pp. 339–352, 2013.
J. R. Kuiper, K. M. O’Brien, K. K. Ferguson, and J. P. Buckley, “Urinary specific gravity measures in the US population: Implications for the adjustment of non-persistent chemical urinary biomarker data,” Environ. Int., vol. 156, p. 106656, 2021.
S. R. Thibodeaux, Updates in Blood Banking and Transfusion Medicine, An Issue of the Clinics in Laboratory Medicine, E-Book, vol. 41, no. 4. Elsevier Health Sciences, 2021.
G. Eknoyan et al., “KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease,” Kidney int, vol. 3, no. 1, pp. 5–14, 2013.
L. A. Inker et al., “KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD,” Am. J. Kidney Dis., vol. 63, no. 5, pp. 713–735, 2014.
K. Yamagata et al., “Chronic kidney disease perspectives in Japan and the importance of urinalysis screening,” Clin. Exp. Nephrol., vol. 12, pp. 1–8, 2008.
R. J. Glassock and A. D. Rule, “The implications of anatomical and functional changes of the aging kidney: with an emphasis on the glomeruli,” Kidney Int., vol. 82, no. 3, pp. 270–277, 2012.
R. J. Glassock and A. D. Rule, “Aging and the kidneys: anatomy, physiology and consequences for defining chronic kidney disease,” Nephron, vol. 134, no. 1, pp. 25–29, 2016.
A. Salekin and J. Stankovic, “Detection of chronic kidney disease and selecting important predictive attributes,” in 2016 IEEE International Conference on Healthcare Informatics (ICHI), 2016, pp. 262–270.
J. C. Fink et al., “Significance of serum creatinine values in new end-stage renal disease patients,” Am. J. kidney Dis., vol. 34, no. 4, pp. 694–701, 1999.
C. Thongprayoon, W. Cheungpasitporn, and K. Kashani, “Serum creatinine level, a surrogate of muscle mass, predicts mortality in critically ill patients,” J. Thorac. Dis., vol. 8, no. 5, p. E305, 2016.
J. Park et al., “Serum creatinine level, a surrogate of muscle mass, predicts mortality in peritoneal dialysis patients,” Nephrol. Dial. Transplant., vol. 28, no. 8, pp. 2146–2155, 2013.
S. J. Schwab, R. L. Christensen, K. Dougherty, and S. Klahr, “Quantitation of proteinuria by the use of protein-to-creatinine ratios in single urine samples,” Arch. Intern. Med., vol. 147, no. 5, pp. 943–944, 1987.
S. D. S. Fraser and T. Blakeman, “Chronic kidney disease: identification and management in primary care,” Pragmatic Obs. Res., pp. 21–32, 2016.
A. S. Sohal, A. S. Gangji, M. A. Crowther, and D. Treleaven, “Uremic bleeding: pathophysiology and clinical risk factors,” Thromb. Res., vol. 118, no. 3, pp. 417–422, 2006.
M. A. Islam, S. Akter, M. S. Hossen, S. A. Keya, S. A. Tisha, and S. Hossain, “Risk factor prediction of chronic kidney disease based on machine learning algorithms,” in 2020 3rd international conference on intelligent sustainable systems (ICISS), 2020, pp. 952–957.
M. IQBAL, “Chronic KIdney Disease dataset,” https://www.kaggle.com/datasets/mansoordaku/ckdisease, 2017. https://www.kaggle.com/mansoordaku/ckdisease.
M. M. Nishat et al., “A comprehensive analysis on detecting chronic kidney disease by employing machine learning algorithms,” EAI Endorsed Trans. Pervasive Heal. Technol., vol. 7, no. 29, pp. e1–e1, 2021.
D. Chhabra, M. Juneja, and G. Chutani, “An efficient ensemble based machine learning approach for predicting Chronic Kidney Disease.,” Curr. Med. Imaging, 2023.
P. Krisanapan, S. Tangpanithandee, C. Thongprayoon, P. Pattharanitima, and W. Cheungpasitporn, “Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence,” Journal of Clinical Medicine, vol. 12, no. 8. MDPI, p. 3018, 2023.
M. J. Raihan, M. A.-M. Khan, S.-H. Kee, and A.-A. Nahid, “Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP,” Sci. Rep., vol. 13, no. 1, p. 6263, 2023.
H. Khalid, A. Khan, M. Zahid Khan, G. Mehmood, and M. Shuaib Qureshi, “Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease,” Comput. Intell. Neurosci., vol. 2023, 2023.
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