Medical imbalanced data classification

Abstract

In general, the imbalanced dataset is a problem often found in health applications. In medical data classification, we often face the imbalanced number of data samples where at least one of the classes constitutes only a very small minority of the data. In the same time, it represent a difficult problem in most of machine learning algorithms. There have been many works dealing with classification of imbalanced dataset. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weights and some rules of thumb to determine those weights. After the balancing phase, we apply the different techniques (Support Vector Machine [SVM], K-Nearest Neighbor [K-NN] and Multilayer perceptron [MLP]) for the balanced datasets. We have also compared the obtained results before and after balancing method. We have obtained best results compared to literature with a classification accuracy of 100%.