# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 2 Alzheimer's Disease  
Alzheimer's disease, the most prevalent form of dementia, is a neurological disorder that impairs reading, writing, reasoning, and memory. According to a 2018 report [19], a new incidence of Alzheimer's is reported every three seconds. This number is projected to increase to 152 million by the year 2050. In 2018, it was anticipated that Alzheimer's disease treatment would cost USD 1 trillion [19]. According to [20], the number of dementia patients in low- and middle-income countries is expanding noticeably faster than in high-income countries. The diagnosis and treatment of Alzheimer's disease lacks a universally applicable or definitive approach [21]. However, a timely diagnosis can substantially mitigate the effects of the disease and preserve the patient's quality of life. Alzheimer's disease is typically diagnosed through physical, neurological, and mental status examinations and a comprehensive evaluation of the patient's medical history. Blood, urine, and genetic tests are administered for evaluation. Other symptoms, including vitamin and nutritional levels, infections, and liver, kidney, and thyroid function, are evaluated by blood or urine testing [22]. Typically, genetic tests are used to determine a family history of dementia. As technology advances, so do medical techniques. Brain scans are one of the methods used to diagnose [PERSON]'s disease. Brain scans that monitor the expansion and contraction of various brain regions are used to diagnose dementia [23]. The expansion or contraction of these regions depends on the [PERSON]'s disease stage and its progression [24]. In these techniques, information is gathered by observing brain regions associated with the hippocampus, cerebral cortex, speaking, remembering, judging, and thinking. The occurrence of [PERSON]'s disease is influenced by several factors, as shown in Figure 1.  
Figure 1: Factors influencing the occurrence of [PERSON]’s disease [Reprinted/adapted with permission from Ref. [12]. Multiple factors contribute to the development of [PERSON]'s disease, including lifestyle, cardiovascular disease, head or brain surgery, age, gender, genetic factors, infection, environmental factors, and latent diseases, such as diabetes. Spherical protein aggregates and stringy protein structures are both produced in the neuronal cell bodies of some [PERSON]'s disease-affected brain regions [24]. The spherical protein structures that [PERSON]'s disease produces are known as amyloid plaques. In [PERSON]'s disease, damaged brain tissue and deformed neuronal components are observed [25]. Figure 2 depicts the altered structure of brain tissue resulting from [PERSON]'s disease, as seen from the diminished gray matter in the brain and areas of plaque that appear in nerve neurons. [PERSON]'s disease can be diagnosed using deep learning and machine learning techniques. Most machine learning and deep learning techniques have been used to classify photographs or the regions within them, and several of these studies are reviewed collectively in this survey. The figure denoted as Figure 3 portrays the architecture of the brain and neurons in an individual in a state of good health. Magnetic resonance imaging of the brain, which utilizes a magnetic field to image brain tissue, is one of the essential techniques for brain tissue analysis. This method investigates the effect of the brain's magnetic field and the external magnetic field, and brain tissue images can be created based on the intensity of protons in the tissues [26]. According to studies, there are other methods for examining brain tissue, such as computed and positron emission tomography. However, magnetic resonance imaging does not harm brain tissue and is therefore rated higher. It utilizes imaging technology to examine brain tissues. Brain diseases such as brain tumors [27], multiple sclerosis [28], and Alzheimer's [29] can be diagnosed and treated using magnetic resonance imaging.