Prediction of kidney disease stages using data mining algorithms

Early detection and characterization are considered to be critical factors in the management and control of chronic kidney disease. Herein, use of efficient data mining techniques is shown to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to ass...

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Veröffentlicht in:	Informatics in medicine unlocked Jg. 15; S. 100178
Hauptverfasser:	Rady, El-Houssainy A., Anwar, Ayman S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 2019 Elsevier
Schlagworte:	Data mining techniques Multilayer perceptron Prediction of kidney disease stages Probabilistic neural networks Radial basis function Support vector machine Radial basis function Multilayer perceptron Prediction of kidney disease stages Support vector machine Probabilistic neural networks Data mining techniques
ISSN:	2352-9148, 2352-9148
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	Early detection and characterization are considered to be critical factors in the management and control of chronic kidney disease. Herein, use of efficient data mining techniques is shown to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist physicians in maximizing accuracy for identification of disease severity stage. The results of applying Probabilistic Neural Networks (PNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) algorithms have been compared, and our findings show that the PNN algorithm provides better classification and prediction performance for determining severity stage in chronic kidney disease.
ISSN:	2352-9148 2352-9148
DOI:	10.1016/j.imu.2019.100178