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Data mining based decision-making approach for predicting survival of kidney dialysis patients
Journal article   Open access   Peer reviewed

Data mining based decision-making approach for predicting survival of kidney dialysis patients

Andrew Kusiak, Shital Shah and Bradley Dixon
IFAC Proceedings Volumes, Vol.36(15), pp.35-39
08/2003
DOI: 10.1016/S1474-6670(17)33468-7
url
https://doi.org/10.1016/S1474-6670(17)33468-7View
Published (Version of record) Open Access

Abstract

Dialysis care is particularly complex and multiple factors may influence patient survival. The cost of such treatment for end stage kidney disease is high and needs attention for reducing it. Individual patient survival may depend on an intricate interrelationship between various demographic and clinical variables, medications, medical interventions and the dialysis treatment prescription. In this research, a data mining approach is used to extract knowledge regarding the interactions between the features and the outcome. There exist a complex and contradictory relationships among data mining rules that are difficult to interpret and implement. To resolve these conflicts a decision-making algorithm is developed using sixteen different classifiers. The decision-making algorithm employs simple and weighted voting schemes. Thus in this paper, a hybrid data mining enhanced decision making approach is used for predictions of an individual patient surviving beyond the median survival time. The concepts introduced in this research have been applied and tested using data collected at four dialysis sites.
 Data Mining Decision Making Dialysis Predictions Survival

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