Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron

Abstract

Imbalanced data is an important problem for medical data analysis. Medical datasets are often not balanced in their class labels. The traditional classifiers can be seriously affected by the imbalanced class distribution in the data. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. This study searches the effect of class imbalance in training data when developing neural network classifier for medical decision making on chronic kidney disease. Neural networks are widely used in a number of applications including data mining and decision systems. Back propagation networks are a popular type of neural networks that can be trained to recognize different patterns. The importance of these networks was considered and a comparative study of some sampling algorithms was performed based on multilayer perceptron with different learning rate values for the prediction of chronic kidney disease. This study reveals that sampling algorithms can improve the performance of classification algorithms and learning rate is a crucial parameter which can significantly effect on multilayer perceptron.