Dataset Information

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

ABSTRACT:

Background

Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.

Objective

To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches.

Methods

The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked.

Results

The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important.

Conclusions

Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.

SUBMITTER: Xiao J

PROVIDER: S-EPMC6458616 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

Xiao Jing J Ding Ruifeng R Xu Xiulin X Guan Haochen H Feng Xinhui X Sun Tao T Zhu Sibo S Ye Zhibin Z

Journal of translational medicine 20190411 1

<h4>Background</h4>Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.<h4>Objective</h4>To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, m ...[more]

PMID: 30971285

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Dataset Information

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

Background

Objective

Methods

Results

Conclusions

Publications

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

Similar Datasets

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