# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 4 Literature Review
The researchers initially used a dataset of 67 MRI images to train the algorithm. Among these images, 38 were obtained from individuals who had been diagnosed with [PERSON]'s disease, while the remaining 29 were acquired from healthy subjects. The goal of the study was to teach the algorithm to distinguish between brains that are suffering from a pathological condition and those that are in a normal physiological state. The researchers partitioned the brain scan images into smaller segments. The optimal size of the divided pieces was determined by manipulating their size across multiple trials. The algorithm underwent evaluation by utilizing neuroimaging data derived from a separate cohort of 148 individuals. The study's sample comprised 100 participants, with 52 categorized exhibiting normal health and 48 diagnosed with Alzheimer's disease. A cohort of 48 participants exhibiting mild cognitive impairment (MCI) were observed to have progressed to a diagnosis of Alzheimer's disease within 2.5 to 9 years. With a precision rate of 86%, the AI algorithm demonstrates a keen ability to distinguish between a brain in a healthy state and a brain affected with Alzheimer's disease. The study exhibited a significant capacity to differentiate between a healthy brain and one with mild cognitive impairment, achieving an accuracy rate of 84%. According to [94; 95], the algorithm exhibits good usefulness, mainly when prophylactic interventions for Alzheimer's disease are present. Initially, the researchers trained the algorithm on a dataset comprising 67 MRI images, of which 38 were obtained from individuals diagnosed with Alzheimer's disease and 29 were acquired from healthy subjects. The objective was to instruct the algorithm to discriminate between diseased and healthy brains. The brain scan images were partitioned into smaller segments by the researchers. Through the manipulation of the size of the divided pieces in various trials, the optimal piece size was determined. Subsequently, the algorithm was evaluated using neuroimaging data obtained from a distinct cohort comprising 148 individuals. The sample population consisted of 100 individuals, of whom 52 were classified as healthy and 48 were diagnosed with Alzheimer's disease. Forty-eight individuals were identified as having a mild cognitive impairment (MLD) and subsequently developed Alzheimer's disease within 2.5 to 9 years. The AI algorithm can distinguish between a healthy brain and a brain afflicted with Alzheimer's with an accuracy rate of 86%. However, it demonstrated a remarkable ability to distinguish between a brain in good health and one with mild cognitive impairment, with an accuracy rate of 84%. The algorithm exhibits potential utility, particularly in the presence of prophylactic interventions for Alzheimer's disease [94; 95]. Prior research has demonstrated that the combination or fusion of multiple modalities of imaging results in an enhancement of either caliber or effectiveness [87]. The objective of this research is to enhance the precision of categorization. The complexity of predicting Alzheimer's disease and the resemblance of brain networks in patients of both categories necessitate extracting a maximum number of features from the brain. Extracting many features poses specific challenges, such as the intricate nature of the block, the abundance of selected features, and the identification of extensive brain regions. Consequently, it is imperative to employ a feature selection and combination approach that minimizes the number of features and selects the optimal ones to enhance binding accuracy. The utilization of pattern recognition techniques, facilitated by the progress of machine learning methodologies, has enabled the automated identification of diseases in diagnostic processes. In addition to offering higher precision and speed, automated diagnostic techniques are comparatively more cost-effective than diagnostic methods that rely on specialists.