# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 1 Introduction  
Alzheimer's disease is a prevalent contributor to cognitive decline in the elderly population. Mild cognitive impairment (MCI) exhibits an annual progression rate to Alzheimer's disease (AD) ranging from 10% to 15% [1]. Mild cognitive impairment (MCI) can be understood as a transitional phase between the cognitive decline associated with dementia and the cognitive abilities typically observed in individuals with normal cognitive functioning. While individuals in good health who have a well-balanced lifestyle and are of the same age usually undergo a mental decline of approximately 1-2% per year, it is essential to note that there is currently no definitive medical diagnosis or treatment available for this condition. However, specific approaches can be employed to impede the progression of this condition [2]. The disease's timely and accurate medical diagnosis inhibits its progression [3]. A group of individuals diagnosed with Alzheimer's disease (AD) and a control group of elderly individuals without the disease were subjected to maze learning using a finger maze task of 10 trials [4]. Following this task, 45 min were allotted to administer verbal learning measures. The participants subsequently executed an extra set of ten attempts on the initial labyrinth, followed by ten shots on a novel maze. The individuals with Alzheimer's disease and a subgroup in the control group exhibited a reduction in the average time taken to complete tasks during the initial two blocks, indicating the acquisition of skills. The results show a significant decrease in the meantime on Block 3 compared to the meantime on Block 1, which may suggest the occurrence of skill generalization. A subset of the controls exhibited a distinct deviation from the abovementioned pattern. The results of the current research suggest that individuals diagnosed with [PERSON]'s disease have the capacity to acquire and employ perceptual motor skills under the guidance of cognitive processes [5]. Numerous methods have been developed thus far to assess the function of the maze. Functional magnetic resonance imaging (fMRI) is the most common method for diagnosing maze defects and their adaptations. Other than the fMRI technique, the remaining methods such as positron emission tomography (PET), electroencephalography (EEG), magnetoencephalography (MEG), near-infrared spectroscopy (NIRS), and transcranial magnetic stimulation (TMS) [6] are identical. Electroencephalography (EEG), magnetoencephalography (MEG), and positron emission tomography (PET) are also utilized to pinpoint the location of neural activity and various brain tumors. The EEG and MEG signals indicate the maze's activity unambiguously. However, the resulting image is a mediator in the fMRI [7] and PET techniques. It is a non-invasive imaging technique with a high spatial resolution that has demonstrated sensitivity to mesenteric lesions in the early phases of the disease. Resting state fMRI (rs-fMRI) is an effective method for comprehending the physiological effects of disease. The fMRI method only examines the function of the maze without contemplating its structure [8]. Alzheimer's is a hazardous, progressive disease affecting the brain and nervous system. Early diagnosis of [PERSON]'s allows for more effective treatments and a powerful and effective treatment strategy [9]. Magnetic resonance imaging (MRI) as a diagnostic tool for identifying plaque and affected regions holds significant value in detecting [PERSON]'s disease [10]. One of the research's primary concerns is identifying [PERSON]'s disease-affected areas using magnetic resonance imaging accurately [11]. The issue can be viewed from two different perspectives. The nature of the classification is the first aspect of the problem, and it must be determined which images have [PERSON]'s disease symptoms and which do not. Another aspect of the problem is the character of the zoning, which necessitates the identification of Alzheimer's-affected areas [12].