Computational Intelligence Based Prediction of Alzheimer Disease from Imbalanced Mild Cognitive Impairment Samples

Abstract

The disease of Alzheimer is a progressive neurological disease. In Alzheimer's disease, as neurons are injured and die throughout the brain. Dementia is the most important and primary concern for Alzheimer’s disease - there is deterioration in memory, thinking, behaviour and the ability to perform everyday activities. In the early stage of memory loss is the result of Mild cognitive impairment (MCI) and it also jeopardise the individual ability of performing his/her independently daily living activities. So, in later stage MCI patients may develop to Alzheimer disease (AD). In this paper a well-known Computational Intelligence tool called Artificial Neural Networks (ANNs) is used to predict AD from MCI samples. Two variants of ANNs - multilayer perceptron (MLP) and radial basis function (RBF) network are used for prediction. We have used 47 MCI samples (previously used by many researchers) for prediction problem. It has been observed that class distributions of MCI dataset are out of proportion i.e. lack of balance (imbalance). We have used oversampling algorithm with cross-validation for imbalanced MCI datasets to predict the developing of Alzheimer's or another dementia. In terms of prediction accuracy our findings are more relevant and it gives better results compared to previous studies which considered without imbalanced scenarios.