Effects of Oversampling SMOTE in the Classification of Hypertensive Dataset

Abstract

Hypertensive or high blood pressure is a medical condition that can be driven by several factors. These factors or variables are needed to build a classification model of the hypertension dataset. In the construction of classification models, class imbalance problems are often found due to oversampling. This research aims to obtain the best classification model by implementing the Support Vector Machine (SVM) method to get the optimal level of accuracy. The dataset consists of 8 features and a label with two classes: hypertensive and non-hypertensive. Overall test result performance is then compared to assess between SVM combined with SMOTE and not. The results show that SMOTE can improve the accuracy of the model for unbalanced data into 98% accuracy compared to 91% accuracy without SMOTE.

Analisa Perbandingan Teknik Oversampling Smote Pada Imbalanced Data

“…Kondisi data tidak seimbang sering menyebabkan tingkat akurasi menjadi rendah. SMOTE bekerja dengan menambahkan data sintetis pada kelas minoritas untuk membuat data menjadi seimbang [5].…”