Have a personal or library account? Click to login

Optimizing urine protein detection accuracy using the K-nearest neighbors algorithm and advanced image segmentation techniques

Open Access
|Jul 2025

References

  1. H. Beng-Ongey, J. S. Robinson, and M. Moxey-Mims, “Chronic kidney disease emerging trends in children and what to do about it,” J. Natl. Med. Assoc., vol. 114, no. 3, Supplement 2, pp. S50–S55, 2022, doi: 10.1016/j.jnma.2022.05.002.
  2. C. A. Herzog et al., “Cardiovascular disease in chronic kidney disease. A clinical update from Kidney Disease: Improving Global Outcomes (KDIGO),” no. July, pp. 572–586, 2011, doi: 10.1038/ki.2011.223.
  3. D. S. Gardner et al., “Urinary Trace Elements Are Biomarkers for Early Detection of Acute Kidney Injury,” Kidney Int. Reports, vol. 7, no. 7, pp. 1524–1538, 2022, doi: 10.1016/j.ekir.2022.04.085.
  4. N. U. Khan et al., “Insights into predicting diabetic nephropathy using urinary biomarkers,” Biochim. Biophys. Acta - Proteins Proteomics, vol. 1868, no. 10, p. 140475, 2020, doi: 10.1016/j.bbapap.2020.140475.
  5. M. Z. Bidin, A. M. Shah, J. Stanslas, and C. T. S. Lim, “Blood and urine biomarkers in chronic kidney disease: An update,” Clin. Chim. Acta, vol. 495, no. April, pp. 239–250, 2019, doi: 10.1016/j.cca.2019.04.069.
  6. M. Hatada, E. Wilson, M. Khanwalker, D. Probst, J. Okuda-Shimazaki, and K. Sode, “Current and future prospective of biosensing molecules for point-of-care sensors for diabetes biomarker,” Sensors Actuators B Chem., vol. 351, no. October 2021, p. 130914, 2022, doi: 10.1016/j.snb.2021.130914.
  7. R. López-Cortés, B. B. Gómez, S. Vázquez-Estévez, D. Pérez-Fentes, and C. Núñez, “Blood-based protein biomarkers in bladder urothelial tumors,” J. Proteomics, vol. 247, no. March, 2021, doi: 10.1016/j.jprot.2021.104329.
  8. A. Yudhana, L. Y. Sabila, A. C. Subrata, H. H. Pratama, and M. S. Akbar, “Non-Invasive Approach for Glucose Detection in Urine Quality Using Its Image Analysis,” V. Asadpour and S. Karakuş, Eds. Rijeka: IntechOpen, 2022, p. Ch. 5.
  9. A. Yudhana, F. Warsino, S. A. Akbar, F. Nuraisyah, and I. Mufandi, “Identification of glucose levels in urine based on classification using-nearest neighbor algorithm method,” Int. J. Smart Sens. Intell. Syst., vol. 16, no. 1, 2023, doi: 10.2478/ijssis-2023-0006.
  10. A. Yudhana and S. A. Akbar, “Self-Health Monitoring Based on Dehydration Level and Glucose Content in Urine using Thermal Sensing,” Int. Conf. Eng. Emerg. Technol., no. October, pp. 27–28, 2021.
  11. A. Yudhana et al., “Multi sensor application-based for measuring the quality of human urine on first-void urine,” Sens. Bio-Sensing Res., p. 100461, Oct. 2021, doi: 10.1016/J.SBSR.2021.100461.
  12. V. Paeder, V. Musi, L. Hvozdara, S. Herminjard, and H. P. Herzig, “Detection of protein aggregation with a Bloch surface wave based sensor,” Sensors Actuators, B Chem., vol. 157, no. 1, pp. 260–264, 2011, doi: 10.1016/j.snb.2011.03.060.
  13. R. Z. Alicic, M. T. Rooney, and K. R. Tuttle, “Diabetic Kidney Disease,” Clin. J. Am. Soc. Nephrol., vol. 12, no. 18, 2017, doi: 10.2215/CJN.11491116.
  14. E. Nah, S. Cho, S. Kim, and H. Cho, “Comparison of Urine Albumin-to-Creatinine Ratio (ACR) Between ACR Strip Test and Quantitative Test in Prediabetes and Diabetes,” Ann. Lab. Med., vol. 37, pp. 28–33, 2017.
  15. J. I. Park, H. Baek, B. R. Kim, and H. H. Jung, “Comparison of urine dipstick and albumin: creatinine ratio for chronic kidney disease screening: A population-based study,” PLoS One, vol. 12, no. 2, pp. 1–12, 2017, doi: 10.1371/journal.pone.0171106.
  16. K. International, “KDIGO clinical practice guidelines for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD),” Kidney Int., vol. 31, no. 2, pp. 1–150, 2013, doi: 10.1038/ki.2009.188.
  17. G. R. D. Jones, “Laboratory reporting of urine protein and albumin,” Clin. Biochem. Rev., vol. 32, no. 2, pp. 103–107, 2011.
  18. A. Bellasi, L. Di Lullo, and B. Di Iorio, “Chronic Kidney Disease: The Silent Epidemy,” J. Clin. Med., vol. 8, pp. 1–8, 2019, doi: 10.3390/jcm8111795.
  19. A.-S. Bargnoux et al., “Evaluation of five immunoturbidimetric assays for urinary albumin quantification and their impact on albuminuria categorization,” Clin. Biochem., vol. 47, no. 16, pp. 250–253, 2014, doi: 10.1016/j.clinbiochem.2014.07.014.
  20. J. W. Brinkman et al., “Which method for quantifying urinary albumin excretion gives what outcome? A comparison of immunonephelometry with HPLC,” Kidney Int., vol. 66, pp. S69–S75, 2004, doi: 10.1111/j.1523-1755.2004.09219.x.
  21. A. R. Kahkoska et al., “The early natural history of albuminuria in young adults with youth-onset type 1 and type 2 diabetes,” J. DIabetes Complicat., vol. 32, no. 12, pp. 1160–1168, 2019, doi: 10.1016/j.jdiacomp.2018.09.018. The.
  22. M. E. Haas et al., “Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure,” Am. J. Hum. Genet., vol. 103, no. 4, pp. 461–473, 2018, doi: 10.1016/j.ajhg.2018.08.004.
  23. J. R. Mejia et al., “Diagnostic accuracy of urine dipstick testing for albumin-to-creatinine ratio and albuminuria: A systematic review and meta-analysis,” Heliyon, vol. 7, no. 11, 2021, doi: 10.1016/j.heliyon.2021.e08253.
  24. H. Ketha and R. J. Singh, “Quantitation of Albumin in Urine by Liquid Chromatography Tandem Mass Spectrometry,” Clin. Appl. Mass Spectrom. Biomol. Anal., pp. 31–36, 2016, doi: 10.1007/978-1-4939-3182-8_4.
  25. W. Laiwattanapaisal et al., “On-Chip Immunoassay for Determination of Urinary Albumin,” Sensors, vol. 1, pp. 10066–10079, 2009, doi: 10.3390/s91210066.
  26. M. A. Cassia et al., “Proteinuria and albuminuria at point of care,” Nephrology, vol. 2, no. 1, pp. 8–16, 2016, doi: 10.5301/pocj.5000194.
  27. D. Zhang et al., “Protein detecting with smartphone-controlled electrochemical impedance spectroscopy for point-of-care applications,” Sensors Actuators B Chem., vol. 222, pp. 994–1002, 2016, doi: 10.1016/j.snb.2015.09.041.
  28. M. N. Azhar, A. Bustam, and F. S. Naseem, “Improving the reliability of smartphone-based urine colorimetry using a colour card calibration method,” Digit. Heal., vol. 9, pp. 1–11, 2023, doi: 10.1177/20552076231154684.
  29. T. Wang, H. Huang, R. Wang, H. Zhou, and P. Luo, “Talanta A feasible image-based colorimetric assay using a smartphone RGB camera for point-of-care monitoring of diabetes,” Talanta, vol. 206, no. August 2019, p. 120211, 2020, doi: 10.1016/j.talanta.2019.120211.
  30. T. Zeng et al., “Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles,” Chinese Chem. Lett., vol. 33, pp. 5184–5188, 2022, doi: 10.1016/j.cclet.2022.03.020.
  31. R. Thakur, P. Maheshwari, S. Kumar Datta, and S. Kumar Dubey, “Smartphone-based, automated detection of urine albumin using deep learning approach,” Meas. J. Int. Meas. Confed., vol. 194, no. January, 2022, doi: 10.1016/j.measurement.2022.110948.
  32. R. Thakur, P. Maheshwari, and S. K. Datta, “Machine Learning-Based Rapid Diagnostic-Test Reader for Albuminuria Using Smartphone,” IEEE Sens. J., vol. 21, no. 13, pp. 14011–14026, 2021, doi: 10.1109/JSEN.2020.3034904.
  33. A. F. Coskun, “Albumin testing in urine using a smart-phone,” NIH Public Access, vol. 13, no. 21, pp. 4231–4238, 2014, doi: 10.1039/c3lc50785h.Albumin.
  34. S. Bhatt, S. Kumar, M. K. Gupta, S. K. Datta, and S. K. Dubey, “Colorimetry-based and smartphone-assisted machine-learning model for quantification of urinary albumin,” Meas. Sci. Technol., vol. 35, no. 1, pp. 1–14, 2023, doi: 10.1088/1361-6501/acfd4c.
  35. F. H. Juwono, W. K. Wong, H. T. Pek, S. Sivakumar, and D. D. Acula, “Ovarian cancer detection using optimized machine learning models with adaptive differential evolution,” Biomed. Signal Process. Control, vol. 77, no. April, p. 103785, 2022, doi: 10.1016/j.bspc.2022.103785.
  36. J. Yu et al., “Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes,” eBioMedicine, vol. 101, p. 105008, 2024, doi: 10.1016/j.ebiom.2024.105008.
  37. R. K. Halder et al., “ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application,” J. Pathol. Inform., vol. 15, no. December 2023, p. 100371, 2024, doi: 10.1016/j.jpi.2024.100371.
  38. K. Hema, K. Meena, and R. Pandian, “Analyze the impact of feature selection techniques in the early prediction of CKD,” Int. J. Cogn. Comput. Eng., vol. 5, no. May 2023, pp. 66–77, 2024, doi: 10.1016/j.ijcce.2023.12.002.
  39. A. S. Zamani, A. H. A. Hashim, A. S. A. Shatat, M. M. Akhtar, M. Rizwanullah, and S. S. I. Mohamed, “Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics,” Biomed. Signal Process. Control, vol. 94, p. 106247, 2024, doi: 10.1016/j.bspc.2024.106247.
  40. M. Mustafizur Rahman, M. Al-Amin, and J. Hossain, “Machine learning models for chronic kidney disease diagnosis and prediction,” Biomed. Signal Process. Control, vol. 87, p. 105368, 2024, doi: 10.1016/j.bspc.2023.105368.
  41. R. Thakur, P. Maheshwari, S. Kumar, and S. Kumar, “Smartphone-based, automated detection of urine albumin using deep learning approach,” Measurement, vol. 194, no. January, p. 110948, 2022, doi: 10.1016/j.measurement.2022.110948.
  42. S. C. Kim and Y. S. Cho, “Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145445.
Language: English
Submitted on: Sep 14, 2024
Published on: Jul 26, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Anton Yudhana, Novi Febrianti, Ilham Mufandi, Arsyad Cahya Subrata, Nuni Ihsana, Son Ali Akbar, Liya Yusrina Sabila, Helda Pratama, Nisa Fajriyanti, Sri Lestari, Ismail Rakip Karas, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.