The Coronavirus Disease (COVID-19) was declared a pandemic disease by the World Health Organization (WHO), and it has not ended so far. Since the infection rate of the COVID-19 increases, the computational approach is needed to predict patients infected with COVID-19 in order to speed up the diagnosis time and minimize human error compared to conventional diagnoses. However, the number of negative data that is higher than positive data can result in a data imbalance situation that affects the classification performance, resulting in a bias in the model evaluation results. This study proposes a new oversampling technique, i.e., TRIM-SBR, to generate the minor class data for diagnosing patients infected with COVID-19. It is still challenging to develop the oversampling technique due to the data’s generalization issue. The proposed method is based on pruning by looking for specific minority areas while retaining data generalization, resulting in minority data seeds that serve as benchmarks in creating new synthesized data using bootstrap resampling techniques. Accuracy, Specificity, Sensitivity, F-measure, and AUC are used to evaluate classifier performance in data imbalance cases. The results show that the TRIM-SBR method provides the best performance compared to other oversampling techniques.
Pruning-Based Oversampling Technique with Smoothed Bootstrap Resampling for Imbalanced Clinical Dataset of Covid-19