# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 4 Literature Review
### Other Methods
Diagnosing neurological diseases poses a significant challenge due to the lack of sensitivity and specificity of structural symptoms and Alzheimer's diagnostic tests. Currently, there is a lack of dependable instruments for forecasting pre-dementia progression. Conversely, the utilization of non-invasive tests for diagnosing neurological disorders is unfeasible due to the resulting impairment of brain tissue, as indicated by reference [133]. [PERSON]'s is a neurodegenerative disorder with a predominantly deleterious impact on memory, cognitive function, and language abilities, ultimately leading to fatality. The condition is induced by the progressive deterioration of neurons in the cerebral cortex. Currently, there is no all-encompassing remedy for the ailment. The only available options are pharmacological intervention or verbal behavior modification to impede its advancement. Research indicates timely intervention in treating Alzheimer's disease can effectively arrest its advancement and preserve optimal cognitive function. The early diagnosis and treatment of Alzheimer's disease pose significant challenges due to the limited manifestation of cognitive impairment in patients and the high incidence of misdiagnosis. Furthermore, individuals at elevated risk for the ailment may exhibit pathological modifications in their cerebral structures before the manifestation of clinical indications of dementia. Hence, the precise identification of Alzheimer's disease patients through the utilization of neuroimaging technologies is crucial to augmenting therapeutic efficacy [134]. Research findings indicate that the human brain comprises an estimated 100 billion neurons and nerve cells. Neurons establish intercellular communication utilizing synapses, which are the gaps between them. The process of neuronal communication is intricate and involves a network with intricate characteristics and stochastic processes [135]. Electroencephalography (EEG) is an imaging modality that is non-invasive and cost-effective. It can capture electrical activity in the brain during neurotransmission with high temporal resolution. The electroencephalography (EEG) technique is utilized in specific medical assistance systems to diagnose [PERSON]'s disease. Increasing evidence suggests that EEG signals could be valuable in differentiating between neurological characteristics and cognitive issues [136]. [PERSON]'s disease is characterized by pathological indications such as heightened oxidative stress, loss of synapses, the gradual emergence of intracellular tau pathology, the accumulation of extracellular amyloid beta plaques, and other related factors [137]. The onset and advancement of [PERSON]'s disease are accompanied by various neurological disorders that culminate in memory loss, significantly impeding individuals' daily functioning. [PERSON]'s disease has been subjected to diverse treatment modalities; however, the results have been unsatisfactory and arduous. The disruption of multiple brain circuits characterizes [PERSON]'s disease, and its diagnosis presents significant challenges. Ongoing research indicates a correlation between a diet high in flavonoids and enhanced cognitive function and mental well-being [138]. Furthermore, research has revealed that several flavonoids can decelerate the advancement of [PERSON]'s disease. Foods rich in flavonoids, such as chocolate, citrus fruits, green tea, and berries, interact with a diverse range of intracellular targets due to the interaction of flavonoids and their metabolites with these targets. Flavonoids exert their neuroprotective effects to preserve neuronal integrity and mitigate the risk of [PERSON]'s disease, as evidenced by previous research [139; 140]. Physicians employ medical imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to aid in the identification of Alzheimer's disease. The PET imaging modality can reveal cerebral lesions' metabolic and functional status.