Exploration of Obesity Status of Indonesia Basic Health Research 2013 With Synthetic Minority Over-Sampling Techniques

Abstract

The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.

Prediction of the Health Status of Older Adults Using Oversampling and Neural Network

“…For example, some studies have used the oversampling techniques to solve the imbalanced problem in the diabetes risk prediction, and experiments have proved that it can effectively enhance the prediction performance of the model [40]. Moreover, the ASUWO algorithm was proposed in 2016 based on the SMOTE oversampling; this algorithm balances the number of samples by sampling with retraction in minority class samples as well as the application of weights to improve the performance of the model on the imbalanced dataset.…”