Diagnosis of the Diseases Using Resampling Methods with Machine Learning Algorithms

Abstract

The rapid diagnosis of diseases is very important for the early start of the treatment process. Pneumonia is a disease that affects the lungs and can cause death in advanced cases. Pneumonia is still today diagnosed by doctors while examining chest X-ray images. As diagnosis of the diseases using machine learning algorithms will be useful. In addition, high success of rate will be obtained while classifying balanced dataset by using machine learning algorithms. Resampling methods are used to balance the dataset by using under-sampling or over-sampling methods. In literature, there is no study comparing under-sampling and over-sampling methods. In the study, an open source dataset was used which included two classes published through the Kaggle data store. The data set includes 1341 healthy and 3875 pneumonia chest X-ray images. Two different resampling methods named Random under-sampling (RU) and ADASYN (Adaptive Synthetic) over-sampling were used while balancing healthy and pneumonia images. After this operation obtained data were used for training machine learning algorithms. In the study, first and second level attributes of the X-ray images in the dataset were used. Logistic Regression (LR) and Support Vector Machine (SVM) algorithms were used for classification of dataset. According to the results obtained, 93.109% accuracy rate of classification success was achieved of X-ray image dataset which balanced by ADASYN over-sampling method with SVM algorithms.