A novel enhanced decision tree model for detecting chronic kidney disease
نویسندگان
چکیده
Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating right patient. Besides, some features distinguish a disease from curable to fatal chronic disease. Data mining techniques have been widely used health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers demonstrated that selection correct increases prediction accuracy. This research work propose method between and non-chronic kidney disease, identify its crucial without reducing prediction, algorithm eliminate possibility under overfitting. study uses recursive feature elimination (RFE) selects an optimal subset ensemble algorithm, enhanced decision tree (EDT), predict results obtained this paper show level EDT changed with removal less significant features, thus enabling decision-makers concentrate on few reduce time treatment. establishes substantially high consistency predicting, selection,
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ژورنال
عنوان ژورنال: Network modeling analysis in health informatics and bioinformatics
سال: 2021
ISSN: ['2192-6662', '2192-6670']
DOI: https://doi.org/10.1007/s13721-021-00302-w