# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 1 Introduction
Existing texture feature extraction methods can be summarized into statistical analysis methods (including the gray-level co-occurrence matrix, run length matrix, and local binary mode, etc. ), structural analysis methods (including syntactic texture description methods, mathematical morphology methods, etc. ), model analysis methods (including the Markov model, fractal model, etc. ), and spectral analysis methods (including the Gabor filter method, Fourier transform method, etc.) [12]. Among them, the structural analysis method is appropriate for texture feature extraction from artificial texture images with regular textures, whereas it is challenging to extract effective features from texture images that cannot extract primitives or texture images with extremely complex arrangement rules. The model analysis method mainly uses model coefficients to identify texture features; however, its solution process is difficult. Thus, for complex brain images with irregular lesion areas, the application of structural analysis and model analysis is very limited. In contrast, statistical analysis methods, such as the gray-level co-occurrence matrix and spectral analysis methods are widely used to extract texture features from brain images [13; 14]. As a result, the gray-level co-occurrence matrix and gray-gradient co-occurrence matrix are used to extract the texture features of medical images of the brain in this paper. Texture features are used to depict the details of lesions more finely in AD images to help classify and identify MRI images of AD. Subsequently, the texture feature and morphological feature are fused to form feature data that can represent image information, laying the foundation of establishing a classification model. In many disease screening and early diagnosis studies, imbalanced classification is the most common challenge when a severely skewed class distribution in the data is attributed to the rarity of the disease. Traditional classification methods that generally assume a balanced class distribution often perform poorly and misclassify subjects from the minority class (i.e., disease) as ones from the majority (i.e., health), resulting in a high false negative rate [15]. In the study of AD, the imbalanced data distribution problem is often encountered. Imbalanced datasets are very unfavorable in the establishment of classification models because the entire establishment process will be biased towards the majority class, resulting in a higher misclassification rate for the minority class [16]. However, in some special cases, the minority class samples are the ones we care most about. For instance, the cost of misdiagnosing a cancer patient as disease-free is far greater than the cost of misdiagnosing a disease-free patient as having cancer in a medical diagnosis [17; 18; 19]. To solve the problem of medical image data with imbalanced data distribution obtained in practical applications, this paper proposes a classification model for AD medical images based on class imbalance learning technology. The experimental data are from the American public data database ADNI (Alzheimer's neuroimaging initiative), which has an obvious class imbalance problem. The research idea is as follows: Firstly, preprocess the brain MRI medical images, determine the ROI, and extract the morphological features and texture features. Secondly, a feature selection algorithm for imbalanced data based on random forests is proposed to solve the class imbalance problem. Finally, extensive experiments are carried out on the ADNI dataset to verify the effectiveness of the proposed method. In this paper, by integrating these methods to study the class imbalance AD brain medical image classification problem, a novel way of AD diagnosis is explored.