Welcome to the InfoSci Platform
Reference Hub2
Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

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

Laxmi Kumari Pathak, Pooja Jha
Copyright: © 2021 |Pages: 21
ISBN13: 9781799866732|ISBN10: 1799866734|ISBN13 Softcover: 9781799866749|EISBN13: 9781799866756
DOI: 10.4018/978-1-7998-6673-2.ch014
Cite Chapter Cite Chapter

MLA

Pathak, Laxmi Kumari, and Pooja Jha. "Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)." Applications of Big Data in Large- and Small-Scale Systems, edited by Sam Goundar and Praveen Kumar Rayani, IGI Global, 2021, pp. 213-233. https://doi.org/10.4018/978-1-7998-6673-2.ch014

APA

Pathak, L. K. & Jha, P. (2021). Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR). In S. Goundar & P. Rayani (Eds.), Applications of Big Data in Large- and Small-Scale Systems (pp. 213-233). IGI Global. https://doi.org/10.4018/978-1-7998-6673-2.ch014

Chicago

Pathak, Laxmi Kumari, and Pooja Jha. "Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)." In Applications of Big Data in Large- and Small-Scale Systems, edited by Sam Goundar and Praveen Kumar Rayani, 213-233. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6673-2.ch014

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.