Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest

Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution, especially for high-dimensional longitudinal data structures. In the medical field, the imbalance of data problem is more common, and correctly identifying samples of the minority class can obtain important information. Moreover, class imbalance in imbalanced AD (Alzheimer's disease) data presents a significant challenge for machine learning algorithms that assume the data are evenly distributed within the classes. In this paper, we propose a random forest-based feature selection algorithm for imbalanced neuroimaging data classification. The algorithm employs random forest to evaluate the value of each feature and combines the correlation matrix to choose the optimal feature subset, which is applied to imbalanced MRI (magnetic resonance imaging) AD data to identify AD, MCI (mild cognitive impairment), and NC (normal individuals). In addition, we extract multiple features from AD images that can represent 2D and 3D brain information. The effectiveness of the proposed method is verified by the experimental evaluation using the public ADNI (Alzheimer's neuroimaging initiative) dataset, and results demonstrate that the proposed method has a higher prediction accuracy and AUC (area under the receiver operating characteristic curve) value in NC-AD, MCI-AD, and NC-MCI group data, with the highest accuracy and AUC value for the NC-AD group data.