Prediction of Renal Cell Carcinoma Based on Ensemble Learning Methods

نویسندگان

چکیده

Objective: In recent years, ensemble learning methods have gained widespread use for early diagnosis of cancer diseases. this study, it is aimed to establish a high-performance model and classification renal cell carcinomas.Methods: the hemogram laboratory data 140 patients with carcinoma without were included in study. The set includes 27 predictors 1 dependent variable. obtained retrospectively. performances machine compared. boosting, bagging, voting stacking as well IB1, IBk, Kstar, LWL, REPTree, Random Forest SMO classifiers compared.Results: REPTree classifier provided highest performance among (Accuracy = 0.867). Among methods, Stacking method Model 6 0.906). performed higher than voting, bagging methods.Conclusion: provide successful results carcinomas. can be used an alternative existing diagnosing carcinoma. order further increase method, recommended choose meta suitable variable types.

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ژورنال

عنوان ژورنال: Middle black sea journal of health science

سال: 2021

ISSN: ['2149-7796']

DOI: https://doi.org/10.19127/mbsjohs.889492