# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 1 Introduction  
Alzheimer's Disease (AD) is the most prevalent neurodegenerative dementia worldwide. It is reported that the number of affected patients is expected to double in the next 20 years, and one in 85 people will be affected by 2050. Thus, the early accurate diagnosis of AD is crucial, as an intermediate stage between normal age-related cognitive decline and dementia, mild cognitive impairment (MCI) has been identified [1]. It may provide a window of chance for treatment and intervention before the disease advances. However, MCI does not always lead to AD and the MCI group is very heterogeneous. It is a highly relevant task to differentiate MCI subjects who have greater risk of developing AD within the next few years from those who will remain stable or even improve [2]. Thus, it is of great interest to identify MCI and also to predict its risk of progressing to AD. Accumulated evidence demonstrates that individuals with AD have both functional and structural changes in their brains, such as loss of gray matter volume [3]. However, these findings are mainly obtained based on group-level statistical comparison, and are thus of limited value for individual-based disease diagnosis. Therefore, some recent studies tried to solve this problem by using magnetic resonance imaging (MRI) and algorithms from machine learning [4; 5]. Computer-based decision shows its efficiency in the detection of the early stage diseases. Recently, many machine learning classification methods have been used for the early diagnosis of AD, including the structural brain atrophy measured by MRI [6; 7; 8]. Data mining approaches, such as principal component analysis (PCA) [9], independent component analysis [10], and support vector machine [11; 12], which can be applied to functional neuroimaging data. Currently, most computer-aided AD diagnostic methods are based on traditional machine learning methods. Traditional machine learning techniques follow a basic assumption; the training and testing data should be under the same distributions. However, in many cases, this assumption does not hold. This excludes a great amount of useful labeled samples as they follow similar but different distributions. To make full use of the labeled samples from similar distributions, to gain knowledge from one context and benefit learning tasks in other contexts, transfer learning is proposed [13; 14; 15]. The earlier application of transfer learning methods have addressed some important issues, including learning how to learn [16], learning one more thing [17], and multi-task learning [18]. [PERSON] and [PERSON] provided a theoretical justification for multi-task learning [19]. [PERSON] and [PERSON] had studied the domain-transfer problem in statistical natural language processing, while using a specific Gaussian model [20]. In some areas, transfer learning has been used in automatic text categorization [21]. Some papers discussed the transfer learning application in robotic soccer system [22; 23]. [PERSON] and [PERSON] introduced a statistical formulation of this problem in terms of a simple mixture model and presented an instance of this framework to maximum entropy classifiers and their linear chain counterparts [24]. Although transfer learning has been widely used in many areas, few people used it in the diagnosis in AD. To establish a transfer learning model for computer-aided clinical diagnosis in AD, five stages of analysis have been outlined in this paper. The first stage is the analysis of MRI results that were obtained from ANDI and a local hospital. 90 anatomical volumes of interest (AVOI) were studied within groups of AD, MCI and NC. In the next stage of the study, we explored the between-group variance within the sample collection. The information gain scales were used in this stage of study. The third stage of this research study was to establish the computer model using transfer learning methods. The data from AD and NC were collected as reference source, which we then transferred to the MCI result group for data classification.