scholarly journals Chronic Kidney Disease Prediction using Machine Learning Models

2019 ◽  
Vol 9 (1) ◽  
pp. 6364-6367
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Failure ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Knowledge Generation ◽  
Good Prediction ◽  
Worst Case ◽  
Wide Range

The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing,data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD

Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

2021 ◽  
pp. 213-233
Author(s):  
Laxmi Kumari Pathak ◽  
Pooja Jha
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Chronic Kidney Disease Risk ◽  
Electronic Health ◽  
Heart Disorders

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

scholarly journals Is It Time for Machine Learning Algorithms to Predict the Risk of Kidney Failure in Patients with Chronic Kidney Disease?

2021 ◽  
Vol 10 (5) ◽  
pp. 1121
Author(s):  
Charat Thongprayoon ◽  
Wisit Kaewput ◽  
Avishek Choudhury ◽  
Panupong Hansrivijit ◽  
Michael A. Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Failure ◽  
Kidney Diseases ◽  
Learning Algorithms ◽  
Clinical Problem ◽  
Machine Learning Algorithms ◽  
Common Clinical Problem

Chronic kidney disease (CKD) is a common clinical problem affecting more than 800 million people with different kidney diseases [...]

scholarly journals Chronic Kidney Disease Prediction based on Blood Potassium Levels using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4194-4199
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Records ◽  
Research Study ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Blood Potassium ◽  
Gain Access

Machine learning is an artificial intelligence(AI) technology that provides the systems with the knowledge and capability to learn and evolve automatically from specifically programmed experiences. This focuses on designing computer programs that are able to gain access and use information on their own. Kidney damage or decreased activity for more than three months is known as chronic kidney disease.This illness occurs when the kidneys can no longer expel extra water or waste from human blood. The goal of this research study is to prepare a predictive modeling for chronic kidney disease data to analyze the different open source python module and output the results predicted by machine learning algorithms and determine the accuracy by comparing different algorithms such as KNN and Logistic Regression which are primarily used for classification of data. This algorithm makes predictions on a dataset collected from the patient's medical records. It gives us the clarity that if someone has chronic kidney disease or not primarily based on a person's blood potassium levels present in their body.

scholarly journals COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251932
Author(s):  
Anna Laura Herzog ◽  
Holger K. von Jouanne-Diedrich ◽  
Christoph Wanner ◽  
Dirk Weismann ◽  
Tobias Schlesinger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Trees ◽  
Critically Ill ◽  
Kidney Failure ◽  
Intensive Care Unit Discharge ◽  
Kidney Tissue ◽  
Great Accuracy ◽  
The Difference

Introduction There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure. Methods We assessed whether proteinuria can be predictive of kidney failure, the development of chronic kidney disease, and mortality in 37 critically ill COVID-19 patients. We used machine learning (ML) methods as decision trees and cut-off points created by the OneR package to add new aspects, even in smaller cohorts. Results Among a total of 37 patients, 24 suffered higher-grade renal failure, 20 of whom required kidney replacement therapy. More than 40% of patients remained on hemodialysis after intensive care unit discharge or died (27%). Due to frequent anuria proteinuria measured in two-thirds of the patients, it was not predictive for the investigated endpoints; albuminuria was higher in patients with AKI 3, but the difference was not significant. ML found cut-off points of >31.4 kg/m2 for BMI and >69 years for age, constructed decision trees with great accuracy, and identified highly predictive variables for outcome and remaining chronic kidney disease. Conclusions Different ML methods and their clinical application, especially decision trees, can provide valuable support for clinical decisions. Presence of proteinuria was not predictive of CKD or AKI and should be confirmed in a larger cohort.

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