Abstract: As one of the major neurodegenerative diseases, Alzheimers disease (AD) has become the most common cause of dementia. Up to now, there is still a lack of effective targeted therapeutic drugs and treatments to prevent the progression of this disease. With the continuous development of computer technology, the use of computer-aided diagnosis technology tools for early AD classification research will provide clinicians with important help. We review the early diagnosis and classification of AD using traditional machine learning and deep learning techniques in recent years. Brain neuroimaging data (such as MRI, PET), electroencephalogram (EEG) and other biomarkers are mainly focused on. By combining machine learning, the classification of early diagnosis of AD is studied. Firstly, the application of machine learning methods to the early AD classification is analyzed, and the classification using different classification algorithms is compared. Secondly, the different biomarkers of subjects using single mode or multi-mode are compared to analyze early AD classification research. Finally, the challenges faced by AD classification are introduced, and future research directions are proposed.

Key words: Alzheimers disease, mild cognitive impairment, machine learning, deep learning, computer aided diagnosis ,