Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree.

Journal: Journal of preventive medicine and hygiene

Published Date: Apr 29, 2021

Abstract

OBJECTIVES: Chronic kidney disease (CKD) is one of the main causes of morbidity and mortality worldwide. Detecting survival modifiable factors could help in prioritizing the clinical care and offers a treatment decision-making for hemodialysis patients. The aim of this study was to develop the best predictive model to explain the predictors of death in Hemodialysis patients by data mining techniques.

Authors

Salman Khazaei

Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
Somayeh Najafi-GhOBADI

Department of Industrial Engineering, Faculty of Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Vajihe Ramezani-Doroh

Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Keywords

Data Mining Decision Trees Humans Logistic Models Neural Networks, Computer Regression Analysis Renal Dialysis Renal Insufficiency, Chronic Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (34322640)

Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree.

Abstract

Authors

Keywords

External Resources

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