Abstract
Background: Chronic kidney disease (CKD) is a progressive and heterogeneous disorder that affects kidney structures and functions. Now it becomes one of the major challenges of public health. Early-stage detection, specialized stage treatments can significantly defer or prevent the progress of CKDs. Currently, clinical CKD stage diagnoses are mainly based on the level of glomerular filtration rate (GFR). However, there are many different equations and approaches to estimate GFR, which can cause inaccurate and contradictory results.
Methods: In this study, we provided a novel method and used machine learning techniques to construct high-performance CKD stage diagnosis models to diagnose CKDs stages without estimating GFR.
Results: We analyzed a dataset of positive metabolite levels in blood samples, which were measured by mass spectrometry. We also developed a feature selection algorithm to identify the most critical and correlated metabolite features related to CKD developments. Then, we used selected metabolite features to construct improved and simplified CKD stage diagnosis models, which significantly reduced the diagnosis cost and time when compared with previous prediction models. Our improved model could achieve over 98% accuracy in CKD prediction. Furthermore, we applied unsupervised learning algorithms to further validate our models and results. Finally, we studied the correlations between the selected metabolite features and CKD developments. The selected metabolite features provided insights into CKD early stage diagnosis, pathophysiological mechanisms, CKD treatments, and drug development.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Levey, A.S., et al.: Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 67(6), 2089–2100 (2005)
Subasi, A., Alickovic, E., Kevric, J.: Diagnosis of chronic kidney disease by using random forest. In: Badnjevic, A. (ed.) CMBEBIH 2017, vol. 62, pp. 589–594. Springer, Heidelberg (2017). https://doi.org/10.1007/978-981-10-4166-2_89
Chawla, L.S., Eggers, P.W., Star, R.A., Kimmel, P.L.: Acute kidney injury and chronic kidney disease as interconnected syndromes. New Engl. J. Med. 371, 5866 (2014)
Bellomo, R., Kellum, J.A., Ronco, C.: Acute kidney injury. Lancet 380, 756766 (2012)
Bagshaw, S.M., Berthiaume, L.R., Delaney, A., Bellomo, R.: Continuous versus intermittent renal replacement therapy for critically ill patients with acute kidney injury: a meta-analysis. Critical Care Med. 36, 610617 (2008)
Levey, A.S., et al.: National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann. Intern. Med. 139(2), 137–147 (2003)
Jha, V., et al.: Chronic kidney disease: global dimension and perspectives. Lancet 382(9888), 260–272 (2013)
Morton, R., Tong, A., Howard, K., Snelling, P., Webster, A.: The views of patients and carers in treatment decision making for chronic kidney disease: systematic review and thematic synthesis of qualitative studies. BMJ 340, c112 (2010)
Coresh, J., et al.: Prevalence of chronic kidney disease in the United States. Jama 298(17), 2038–2047 (2007)
Lameire, N., Van Biesen, W.: The initiation of renal-replacement therapy: Just-in-time delivery. New Engl. J. Med. 363, 678680 (2010)
Levey, A.S., Coresh, J.: Chronic kidney disease. Lancet 379, 165180 (2012)
Levin, A., et al.: Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 3, 150 (2013)
Webster, A.C., Nagler, E.V., Morton, R.L., Masson, P.: Chronic kidney disease. Lancet 389, 12381252 (2017)
National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am. J. Kidney Dis.: Official J. Natl. Kidney Found. 39, S1 (2002)
Bevc, S., et al.: Estimation of glomerular filtration rate in elderly chronic kidney disease patients: comparison of three novel sophisticated equations and simple cystatin C equation. Ther. Apher. Dial. 21, 126–132 (2017)
Levey, A.S., et al.: A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150(9), 604–612 (2009)
Canales, M.T., et al.: Renal function and death in older women: which eGFR formula should we use? Int. J. Nephrol. 2017, 10 (2017)
Neves, J., et al.: A soft computing approach to kidney diseases evaluation. J. Med. Syst. 39(10), 131 (2015)
Polat, H., Mehr, H.D., Cetin, A.: Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. J. Med. Syst. 41, 55 (2017)
Celik, E., Atalay, M., Kondiloglu, A.: The diagnosis and estimate of chronic kidney disease using the machine learning methods. Int. J. Intell. Syst. Appl. Eng., 27–31 (2016)
Chen, Z., Zhang, X., Zhang, Z.: Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models. Int. Urol. Nephrol. 48, 20692075 (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Weiss, R.H., Kim, K.: Metabolomics in the study of kidney diseases. Nat. Rev. Nephrol. 8, 2233 (2012)
Shah, V.O., Townsend, R.R., Feldman, H.I., Pappan, K.L., Kensicki, E., Vander Jagt, D.L.: Plasma metabolomic profiles in different stages of CKD. Clin. J. Am. Soc. Nephrol. 8(3), 363–370 (2013)
McMahon, G.M., et al.: Urinary metabolites along with common and rare genetic variations are associated with incident chronic kidney disease. Kidney Int. 91(6), 1426–1435 (2017)
Scialla, J.J., et al.: Mineral metabolites and CKD progression in African Americans. J. Am. Soc. Nephrol. 24, 125135 (2013)
Waskom, M., et al.: seaborn: v0.7.1, June 2016. Zenodo. https://doi.org/10.5281/zenodo.54844. Chicago
McKinney, W.: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. OReilly Media Inc, Sebastopol (2012)
Funding
Cancer Center Supporting Grant from the National Cancer Institute (P30CA118100). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Author information
Authors and Affiliations
Contributions
JT, YG, and BF conceived and designed the project. BF, HY, JW, SP, YG, and JT designed and performed the experiments. All authors analyzed the experiments results of this project. BF wrote the manuscript. All authors reviewed the manuscript. All authors read and approved the manuscript.
Corresponding authors
Editor information
Editors and Affiliations
Ethics declarations
This study was approved by the Ethical Committee of Northwest University, Xi’an, China. All patients provided informed consent prior to entering the study.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, B. et al. (2019). The Application of Machine Learning Algorithms to Diagnose CKD Stages and Identify Critical Metabolites Features. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-17938-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17937-3
Online ISBN: 978-3-030-17938-0
eBook Packages: Computer ScienceComputer Science (R0)