Artificial Intelligence Predicts Progress of Diabetic Kidney Disease-Novel Prediction Model Construction with Big Data Machine Learning

Abstract BackgroundMicroalbuminuria is the main characteristic of Diabetic kidney disease (DKD), but it fluctuates greatly under the influence of blood glucose. Our aim was to establish some common clinical variables which could be easily collected to predict the risk of DKD in patients with type 2 diabetes. Methods and resultsWe build an artificial intelligence (AI) model to quantitively predict the risk of DKD based on the biomedical parameters from 1239 patients. An information entropy-based feature selection method was applied to screen out the risk factors of DKD. The dataset was divided with 4/5 into the training set and 1/5 into the test set. By using the selected risk factors, 5-fold cross-validation is applied to train the prediction model and it finally got AUC of 0.72 and 0.71 in the training set and test set respectively. In addition, we provide a method of calculating risk factors’ contribution for individuals to provide personalized guidance for treatment. We set up web-based application available on http://www.cuilab.cn/dkd for self-check and early warning. ConclusionsWe establish a feasible prediction model for DKD and suggest the degree of risk contribution of each indicator for each individual, which has certain clinical significance for early intervention and prevention.

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Decision letter for "Machine Learning based Early Prediction of End‐stage Renal Disease in Patients with Diabetic Kidney Disease using Clinical Trials Data"

10.1111/dom.14178/v3/decision1 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Kidney Disease ◽

Renal Disease ◽

End Stage Renal Disease ◽

Diabetic Kidney Disease ◽

Early Prediction ◽

Diabetic Kidney ◽

Stage Renal Disease ◽

End Stage

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Review for "Machine Learning based Early Prediction of End‐stage Renal Disease in Patients with Diabetic Kidney Disease using Clinical Trials Data"

10.1111/dom.14178/v2/review1 ◽

2020 ◽

Author(s):

kalung chan

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Kidney Disease ◽

Renal Disease ◽

End Stage Renal Disease ◽

Diabetic Kidney Disease ◽

Early Prediction ◽

Diabetic Kidney ◽

Stage Renal Disease ◽

End Stage

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Review for "Machine Learning based Early Prediction of End‐stage Renal Disease in Patients with Diabetic Kidney Disease using Clinical Trials Data"

10.1111/dom.14178/v2/review2 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Kidney Disease ◽

Renal Disease ◽

End Stage Renal Disease ◽

Diabetic Kidney Disease ◽

Early Prediction ◽

Diabetic Kidney ◽

Stage Renal Disease ◽

End Stage

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Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach

Diagnostics ◽

10.3390/diagnostics9040133 ◽

2019 ◽

Vol 9 (4) ◽

pp. 133 ◽

Cited By ~ 2

Author(s):

Yoichi Hayashi

Keyword(s):

Artificial Intelligence ◽

Kidney Disease ◽

Early Development ◽

Diabetic Kidney Disease ◽

Rule Extraction ◽

Self Report ◽

Test Accuracy ◽

Operating Characteristics ◽

Large Area ◽

Diabetic Kidney

The aim of the present study was to determine the lowest cut-off value for albuminuria levels, which can be used to detect diabetic kidney disease (DKD) using the urinary albumin-to-creatinine ratio (UACR). National Health and Nutrition Examination Survey (NHANES) data for US adults were used, and participants were classified as having diabetes or prediabetes based on a self-report and physiological measures. The study dataset comprised 942 diabetes and 524 prediabetes samples. This study clarified the significance of the lower albuminuria (UACR) levels, which can detect DKD, using an artificial intelligence-based rule extraction approach. The diagnostic rules (15 concrete rules) for both samples were extracted using a recursive-rule eXtraction (Re-RX) algorithm with continuous attributes (continuous Re-RX) to discriminate between prediabetes and diabetes datasets. Continuous Re-RX showed high test accuracy (77.56%) and a large area under the receiver operating characteristics curve (75%), which derived the two cut-off values (6.1 mg/g Cr and 71.00 mg/g Cr) for the lower albuminuria level in the UACR to detect early development of DKD. The early cut-off values for normoalbuminuria (NA) and microalbuminuria (MA) will be determined to help detect CKD and DKD, and to detect diabetes before MA develop and to prevent diabetic complications.

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