Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus

Most people with diabetes in the world are type 2. We can detect diabetes early to prevent things that are not desirable by checking sugar and insulin levels with the doctor. In addition to using this method, people with diabetes can also be grouped based on data from diabetes examination results. However, most of the data on health examination results have several parameters that are difficult for the public to understand. These problems can be done by means of automatic classification. In addition to these problems, there is another problem in the form of an unbalanced amount of data for diabetics and non-diabetics. This problem can be done by balancing the amount of data using the model to increase the ratio of the amount of data that is small or decrease the ratio of the amount of data that is too much. Purpose: This study aims to detect type 2 diabetes mellitus using the SVM classification model and analyze the results of the comparison using the SMOTE and ADASYN data balancing technique which is the best. Methods/Study design/approach: The research method starts from collecting the diabetes dataset, then the dataset cleaning process is carried out whether there is a null value or not. After applying two oversampling methods to analyze which method is the most appropriate. After the oversampling technique was carried out, data classification was carried out using a support vector machine model to see the accuracy results. Result/Findings: The results obtained by the ADASYN-SVM method are superior to SMOTE-SVM. The ADASYNSVM method has an accuracy of 87.3%, while the SMOTE-SVM has an accuracy of 85.4%. Novelty/Originality/Value: The data used in this study came from the Karya Medika clinic, Indonesia which contains parameters related to type 2 diabetes.