SAT-084 USE OF AN AI ALGORITHM AND MACHINE LEARNING FOR SCREENING AND EARLY DETECTION OF CHRONIC KIDNEY DISEASE

Abstract Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages i.e., early stage to the last stage of kidney failure. Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. In particular, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with a 85.5% accuracy. The study also showed that J48 shows improved performance over Random Forest, so, it may be used to build an automated system for the detection of severity of CKD.

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A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease

Diagnostics ◽

10.3390/diagnostics12010116 ◽

2022 ◽

Vol 12 (1) ◽

pp. 116

Author(s):

Vijendra Singh ◽

Vijayan K. Asari ◽

Rajkumar Rajasekaran

Keyword(s):

Neural Network ◽

Machine Learning ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Early Detection ◽

Deep Neural Network ◽

Missing Values ◽

Machine Learning Techniques ◽

Recursive Feature Elimination ◽

Support Vector

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network’s optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD.

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Chronic kidney disease diagnosis using decision tree algorithms

BMC Nephrology ◽

10.1186/s12882-021-02474-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hamida Ilyas ◽

Sajid Ali ◽

Mahvish Ponum ◽

Osman Hasan ◽

Muhammad Tahir Mahmood ◽

...

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Random Forest ◽

Kidney Disease ◽

Early Detection ◽

Disease Diagnosis ◽

Automated System ◽

Disease Epidemiology ◽

Machine Learning Classification ◽

Improved Performance

Abstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.

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Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm

Applied Sciences ◽

10.3390/app11010202 ◽

2020 ◽

Vol 11 (1) ◽

pp. 202

Author(s):

Weilun Wang ◽

Goutam Chakraborty ◽

Basabi Chakraborty

Keyword(s):

Public Health ◽

Machine Learning ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Early Detection ◽

Regression Model ◽

Health Examination ◽

Test Results ◽

Tree Model ◽

The Impact

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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Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat

Journal of Feline Medicine and Surgery ◽

10.1177/1098612x211001273 ◽

2021 ◽

pp. 1098612X2110012

Author(s):

Jade Renard ◽

Mathieu R Faucher ◽

Anaïs Combes ◽

Didier Concordet ◽

Brice S Reynolds

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Decision Tree ◽

Diagnostic Performance ◽

Clinical Signs ◽

Learning System ◽

Clinical Severity ◽

Medium Term ◽

Term Survival

Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea48), spontaneous feeding at 48 h (SpF48) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48. Conclusions and relevance Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.

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