Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals
Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and reg...
Main Authors: | Chin-Chuan Shih, Chi-Jie Lu, Gin-Den Chen, Chi-Chang Chang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-07-01
|
Series: | International Journal of Environmental Research and Public Health |
Subjects: | |
Online Access: | https://www.mdpi.com/1660-4601/17/14/4973 |
Similar Items
-
Predictive analysis of chronic kidney disease based on machine learning
by: Huan You
Published: (2021-03-01) -
Prediction of Chronic Kidney Disease - A Machine Learning Perspective
by: Pankaj Chittora, et al.
Published: (2021-01-01) -
Chronic Kidney Disease – Where Next? Predicting Outcomes and Planning Care Pathways
by: Angharad Marks, et al.
Published: (2014-07-01) -
Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques
by: Firouzeh Moeinzadeh, et al.
Published: (2021-09-01) -
Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia
by: Yeli Wang, et al.
Published: (2019-12-01)