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Network Modeling Analysis in Health Informatics and Bioinformatics
Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics 1/2021

01.12.2021 | Original Article

A novel enhanced decision tree model for detecting chronic kidney disease

verfasst von: Avijit Kumar Chaudhuri, Deepankar Sinha, Dilip K. Banerjee, Anirban Das

Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2021

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Abstract

Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating the right patient. Besides, some features distinguish a disease from curable to fatal or curable to chronic disease. Data mining techniques have been widely used in health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers have demonstrated that the selection of correct features increases the prediction accuracy. This research work propose a method to distinguish between chronic and non-chronic kidney disease, identify its crucial features without reducing the accuracy of prediction, and a prediction algorithm to eliminate the possibility of under or overfitting. This study uses the recursive feature elimination (RFE) method that selects an optimal subset of features and an ensemble algorithm, the enhanced decision tree (EDT), to predict the disease. The results obtained in this paper show that the accuracy level of EDT is not changed with the removal of less significant features, thus enabling the decision-makers to concentrate on few features to reduce time and error of treatment. EDT establishes substantially high consistency in predicting, with or without feature selection, the disease.
Vorheriger Artikel Binding ability of arginine, citrulline, N-acetyl citrulline and thiocitrulline with SARS COV-2 main protease using molecular docking studies
Nächster Artikel Exploration of targets and molecular mechanisms of cinnamaldehyde in overcoming fulvestrant-resistant breast cancer: a bioinformatics study

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Metadaten
Titel
A novel enhanced decision tree model for detecting chronic kidney disease
verfasst von
Avijit Kumar Chaudhuri
Deepankar Sinha
Dilip K. Banerjee
Anirban Das
Publikationsdatum
01.12.2021
Verlag
Springer Vienna
Erschienen in
Network Modeling Analysis in Health Informatics and Bioinformatics / Ausgabe 1/2021
Print ISSN: 2192-6662
Elektronische ISSN: 2192-6670
DOI
https://doi.org/10.1007/s13721-021-00302-w

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