Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for indirectly determining GFR and then the stage of chronic kidney disease (CKD). Adding a creatinine test into routine he...
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| Vydané v: | Applied Sciences Ročník 11; číslo 1; s. 202 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Basel
MDPI AG
01.01.2021
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for indirectly determining GFR and then the stage of chronic kidney disease (CKD). Adding a creatinine test into routine health examination could detect CKD. As more items for comprehensive examination means higher cost, creatinine testing is not included in the routine health examination in many countries. An algorithm based on common test results, without creatinine test, to evaluate the risk of CKD will increase the chance of its early detection and treatment. Methods: In this study, we used open source data containing 1 million samples. These data contain 23 health-related features, including common diagnostic test results provided by National Health Insurance Sharing Service (NHISS). A low GFR indicates possible chronic kidney disease (CKD). As is commonly accepted in the medical community, a GFR of 60 mL/min is used as the threshold, below which is considered to have CKD. In this study, the first step aims to build a regression model to predict the value of creatinine from 23 features, and then combine the predicted value of creatinine with the original 23 features to evaluate the risk of CKD. We will show by simulation that by the proposed method we can achieve better prediction results compared to direct prediction from 23 features. The data is extremely unbalanced for predicting the target variable creatinine. We used undersampling method and proposed a new cost-sensitive mean-squared error (MSE) loss function to deal with the problem. Regrading model selection, this work used three machine learning models: a bagging tree model named Random Forest, a boosting tree model named XGBoost, and a neural network based model named ResNet. To improve the result of the creatinine predictor, we averaged results from eight predictors, a method known as ensemble learning. Finally, the predicted creatinine and the original 23 features is used to predict the risk of CKD. Results: We optimized results of R-Squared (R2) value to select the appropriate undersampling strategy and the regression model for the regression stage of creatinine prediction. Ensembled model achieved the best performance of R2 of 0.5590. The six factors from 23 are selected from the top of the list of how strongly they affect the creatinine value. They are sex, age, hemoglobin, the level of urine protein, waist circumference, and habit of smoking. Using the predicted value of creatinine, an area under Receiver Operating Characteristic curve (AUC) of 0.76 is achieved while classifying samples for CKD. Conclusions: Using commonly available health parameters, the proposed system can assess the risk of CKD for public health. High-risk subjects can be screened and advised to take a creatinine test for further confirmation. In this way, we can reduce the impact of CKD on public health and facilitate early detection for many, where a blanket test of creatinine is not available for all. |
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| AbstractList | Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for indirectly determining GFR and then the stage of chronic kidney disease (CKD). Adding a creatinine test into routine health examination could detect CKD. As more items for comprehensive examination means higher cost, creatinine testing is not included in the routine health examination in many countries. An algorithm based on common test results, without creatinine test, to evaluate the risk of CKD will increase the chance of its early detection and treatment. Methods: In this study, we used open source data containing 1 million samples. These data contain 23 health-related features, including common diagnostic test results provided by National Health Insurance Sharing Service (NHISS). A low GFR indicates possible chronic kidney disease (CKD). As is commonly accepted in the medical community, a GFR of 60 mL/min is used as the threshold, below which is considered to have CKD. In this study, the first step aims to build a regression model to predict the value of creatinine from 23 features, and then combine the predicted value of creatinine with the original 23 features to evaluate the risk of CKD. We will show by simulation that by the proposed method we can achieve better prediction results compared to direct prediction from 23 features. The data is extremely unbalanced for predicting the target variable creatinine. We used undersampling method and proposed a new cost-sensitive mean-squared error (MSE) loss function to deal with the problem. Regrading model selection, this work used three machine learning models: a bagging tree model named Random Forest, a boosting tree model named XGBoost, and a neural network based model named ResNet. To improve the result of the creatinine predictor, we averaged results from eight predictors, a method known as ensemble learning. Finally, the predicted creatinine and the original 23 features is used to predict the risk of CKD. Results: We optimized results of R-Squared (R2) value to select the appropriate undersampling strategy and the regression model for the regression stage of creatinine prediction. Ensembled model achieved the best performance of R2 of 0.5590. The six factors from 23 are selected from the top of the list of how strongly they affect the creatinine value. They are sex, age, hemoglobin, the level of urine protein, waist circumference, and habit of smoking. Using the predicted value of creatinine, an area under Receiver Operating Characteristic curve (AUC) of 0.76 is achieved while classifying samples for CKD. Conclusions: Using commonly available health parameters, the proposed system can assess the risk of CKD for public health. High-risk subjects can be screened and advised to take a creatinine test for further confirmation. In this way, we can reduce the impact of CKD on public health and facilitate early detection for many, where a blanket test of creatinine is not available for all. |
| Author | Weilun Wang Goutam Chakraborty Basabi Chakraborty |
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| Cites_doi | 10.7326/0003-4819-158-11-201306040-00007 10.1186/s12882-017-0497-6 10.1016/j.imu.2019.100178 10.1038/s41746-019-0104-2 10.1159/000489897 10.14257/ijmue.2017.12.8.03 10.3390/e22020193 10.1145/2939672.2939785 10.1016/j.neunet.2018.07.011 10.1038/ki.2011.368 10.1109/ICHI.2016.36 10.35940/ijitee.L3572.1081219 10.1214/aos/1013203451 10.1016/j.compbiomed.2019.04.017 10.14569/IJACSA.2019.0100813 |
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| References_xml | – volume: 5 start-page: 24 year: 2016 ident: ref_8 article-title: Prediction of chronic kidney disease using random forest machine learning algorithm publication-title: Int. J. Comput. Sci. Mob. Comput. – volume: 158 start-page: 825 year: 2013 ident: ref_1 article-title: Evaluation and management of chronic kidney disease: Synopsis of the kidney disease: Improving global outcomes 2012 clinical practice guideline publication-title: Ann. Intern. Med. doi: 10.7326/0003-4819-158-11-201306040-00007 – ident: ref_4 – ident: ref_5 doi: 10.1186/s12882-017-0497-6 – volume: 15 start-page: 100178 year: 2019 ident: ref_9 article-title: Prediction of kidney disease stages using data mining algorithms publication-title: Inform. Med. Unlocked doi: 10.1016/j.imu.2019.100178 – volume: 2 start-page: 1 year: 2019 ident: ref_6 article-title: Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0104-2 – volume: 139 start-page: 313 year: 2018 ident: ref_2 article-title: Disparities in chronic kidney disease prevalence among males and females in 195 countries: Analysis of the Global Burden of Disease 2016 Study publication-title: Nephron doi: 10.1159/000489897 – volume: 12 start-page: 23 year: 2017 ident: ref_10 article-title: Risk Level Prediction of Chronic Kidney Disease Using Neuro-Fuzzy and Hierarchical Clustering Algorithm (s) publication-title: Int. J. Multimedia Ubiq. Eng. doi: 10.14257/ijmue.2017.12.8.03 – ident: ref_17 doi: 10.3390/e22020193 – ident: ref_14 – ident: ref_16 doi: 10.1145/2939672.2939785 – volume: 106 start-page: 249 year: 2017 ident: ref_19 article-title: A systematic study of the class imbalance problem in convolutional neural networks publication-title: Neural Netw. doi: 10.1016/j.neunet.2018.07.011 – volume: 80 start-page: 1258 year: 2011 ident: ref_3 article-title: The contribution of chronic kidney disease to the global burden of major noncommunicable diseases publication-title: Kidney Int. doi: 10.1038/ki.2011.368 – ident: ref_12 doi: 10.1109/ICHI.2016.36 – ident: ref_7 doi: 10.35940/ijitee.L3572.1081219 – volume: 2 start-page: 18 year: 2002 ident: ref_15 article-title: Classification and regression by randomForest publication-title: R News – ident: ref_18 doi: 10.1214/aos/1013203451 – volume: 109 start-page: 101 year: 2019 ident: ref_11 article-title: Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.04.017 – ident: ref_13 doi: 10.14569/IJACSA.2019.0100813 |
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| Title | Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm |
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