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Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models

  • Nephrology - Original Paper
  • Published:
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Abstract

Purpose

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring.

Methods

Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD.

Results

For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models.

Conclusions

Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

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Acknowledgments

The UCI Machine Learning Repository and the CKD data donators are thanked.

Funding

This work is partly supported by the National Natural Science Foundation of China (21275101). The funding agency had no role in the study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit this paper for publication.

Author information

Authors and Affiliations

  1. Department of Chemistry, Capital Normal University, 105 West 3rd Ring Road North, Beijing, 100048, China

    Zewei Chen, Xin Zhang & Zhuoyong Zhang

Authors
  1. Zewei Chen
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  2. Xin Zhang
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  3. Zhuoyong Zhang
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Corresponding author

Correspondence to Zhuoyong Zhang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Cite this article

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, 2069–2075 (2016). https://doi.org/10.1007/s11255-016-1346-4

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  • DOI: https://doi.org/10.1007/s11255-016-1346-4

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