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Mediana Aryuni,Suko Adiarto,Eka Miranda,Evaristus Didik Madyatmadja,Albert Verasius Dian Sano,Elvin Sestomi 한국지능시스템학회 2023 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.23 No.2
In the field of medical data mining, imbalanced data categorization occurs frequently, whichtypically leads to classifiers with low predictive accuracy for the minority class. This studyaims to construct a classifier model for imbalanced data using the SMOTE oversamplingalgorithm and a heart disease dataset obtained from Harapan Kita Hospital. The categorizationmodel utilized logistic regression, decision tree, random forest, bagging logistic regression,and bagging decision tree. SMOTE improved the model prediction accuracy with imbalanceddata, particularly for minority classes