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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Difa Fitria
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Classification of Appendicitis Disease in Children Using SVM with KNN Imputation and SMOTE Approach Difa Fitria; Triando Hamonangan Saragih; Muliadi; Dwi Kartini; Fatma Indriani
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.470

Abstract

This study evaluates the effect of SMOTE and KNN imputation techniques on the performance of SVM classification models on a nearly balanced dataset. The results show that using SMOTE increases model precision but decreases recall. This shows the importance of careful consideration when choosing data processing strategies to achieve optimal classification model performance. This study evaluates the effect of the Synthetic Minority Over-sampling Technique (SMOTE) and K-Nearest Neighbors (KNN) imputation on the performance of Support Vector Machine (SVM) classification models on nearly balanced datasets. The results of this study noted that the use of SMOTE techniques in balancing the dataset led to a decrease in classification model accuracy from 87.26% to 85.99%. However, there was a slight increase in AUC-ROC, from 85.96% to 88.04%. The results of this study noted that the use of the SMOTE technique in balancing the dataset caused a decrease in the accuracy of the classification model from 87.26% to 85.99%. However, there was an improvement in the AUC-ROC, from 85.96% to 88.04%.
Co-Authors Dwi Kartini Fatma Indriani Muliadi Saragih, Triando Hamonangan