# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 1 Introduction  
AD (Alzheimer's disease) is a chronic neurodegenerative disease and has become the most common form of dementia in the elderly. In clinical diagnoses, AD patients are characterized by a significant decline in memory, loss of language ability, cognitive function decline, and gradual decline in self-care ability, etc., accompanied by the emergence of dementia, such as mental disorders, which seriously threatens the quality of life and life health of the elderly [1]. According to statistics, there are currently more than 50 million AD patients in the world. With the increasing aging of the global population, the number of elderly people has increased significantly. It is estimated that the number of AD patients in the world will increase to 152 million by 2050 [2]. MCI (mild cognitive impairment) is generally considered to be the early stage of AD. Based on relevant research reports, 10-15% of MCI patients develop into AD each year; however, only 2% of the normal population (normal people) develop into AD each year. Thus, MCI patients are highly susceptible to developing AD [3, 4]. This makes the MCI stage an ideal target for early prediction, as research suggests that early diagnosis is the key to potentially delayingthe overall progression of AD. However, currently, there is no effective method of curing AD; thus, early diagnosis and treatment for patients have a great significance in delaying the development of AD. With the rapid development of machine learning technology, various computer technologies are used to process and analyze brain images to extract their essential features and combine machine learning methods to design a method that can automatically and accurately diagnose MCI, AD, and NC, which aids in the rapid and accurate diagnosis of the disease so that AD patients can be treated at an early stage to delay or prevent a further progression of the disease process. By analyzing the changes in the brain neuroimaging structure and function of AD patients, many researchers have attempted to identify biomarkers that can contribute to the clinical diagnosis of AD and offer value for early diagnosis. MRI (magnetic resonance imaging) is widely used in the early diagnosis of AD, due to its advantages of non-invasiveness and high popularity [5]. The framework of a brain imaging-based classification model consists primarily of three parts: image preprocessing, image feature extraction, and data processing. Among them, image feature extraction plays a crucial role, which is commonly used in natural images including color features, texture features, or shape features. Most of the different types of images collected by the existing medical imaging equipment are non-color images. Therefore, compared with color features, texture features or shape features are more important for medical images. Since subtle changes in brain histopathology may cause significant changes in the function of brain regions, the analysis of morphological features in medical images has gradually received attention in recent years. It utilizes non-invasive morphological MRI techniques to study changes in brain tissue structure [6], such as the VBM (voxel-based morphometry) method [7] and the manually marked ROI (regions of interest) method [8]. Compared with the ROI method, the VBM method is a hypothetical, efficient, and unbiased method that can measure and analyze differences between brain tissues, such as gray and white matter in the whole brain [7]. Therefore, VBM is widely used to assess morphological changes [9], which has been successfully applied to study the gray matter changes of AD [10; 11]. Texture analysis defines the quantification of the grayscale pattern of the image, which embodies the surface structure and organization properties of the object surface of periodic changes. It can help identify the surface phenomenon of the image that exhibits different changes.