Functional magnetic resonance imaging classification based on random forest algorithm in Alzheimer's disease

For classifying Alzheimer's disease (AD) by analyzing medical image data, in this paper a computer-aided diagnosis method is proposed based on random forest algorithm. In this study functional magnetic resonance imaging (fMRI) data including 34 AD patients, 35 mild cognitive impairments (MCI) and 35 normal controls (NC) is collected. Firstly, functional connection between the different regions of whole brain is calculated using Pearson correlation coefficient. Then the importance of the functional connection between different brain regions is measured and the important features are selected using the random forest algorithm. Finally, classification is performed using support vector machine (SVM) classifier with ten-fold cross-validation. The classification model based on random forest and SVM has a good effect on the recognition of AD, and the classification accuracy rate can reach 90.68%. Functional connection characteristics can be effectively analyzed by the random forest algorithm which can distinguish AD, MCI and NC accurately. At the same time, the abnormal brain regions of AD pathogenesis can be obtained. The related experimental results can provide an objective reference for the early clinical diagnosis of AD.