Medical data classification scheme based on hybridized SMOTE technique (HST) and Rough Set technique (RST)

Medical data are extensively used in the diagnosis of human health. So it has played a vital role for physicians as well as in medical engineering. Accordingly, many types of research are going on related to this to have a better prediction of the diseases or to improve the diagnosis quality. However, most of the researchers work on either dimensionality space or imbalanced data. Due to this, sometimes one may not have the accurate predictions or classifications of the malignant diseases as both the factors are equally important. So it still needs an improvement or more work required to address these biomedical challenges by combing both the factors. As such this paper proposes a new and efficient algorithm that combining the Rough Set Theory and Maximum Distance Based on Synthetic Minority which successfully deal with imbalanced data by increases the minority class and ignoring the majority class. The present algorithm has been investigated on biomedical data and it gives the desired results in terms of dimensionality and data balancing. Here, In this paper, the quality of balanced data on minority class has been evaluated using assessment metrics like co-variance, Accuracy (ACC) and Area Under the Curve (AUC). It has been observed from the numerical results that the performance of the algorithm achieved the best accuracy with metrics of ACC and AUC.