# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 4 Literature Review
### Support Vector Machine  
In [116], the use of support vector machines as a classification technique and for improved feature selection in diagnosing Alzheimer's disease is presented as a structured approach. The method's accuracy is reported to be 92.48%. The sensitivity and feature rates are reported to be 86.92% and 90.76%, respectively. The paper's authors have introduced a methodology that employs principal component analysis to determine feature rank, coupled with [PERSON]'s classification approach. This method yields an accuracy of 96.32%, a sensitivity of 94.11%, and a feature rate of 98.52% [117]. The article referenced [118] outlines a methodology for diagnosing Alzheimer's disease utilizing image processing techniques and genetic algorithms for classification and prediction purposes. The present study involves the conversion of Alzheimer's disease into a cognitive disorder, which serves as the primary characteristic of the input MRI images. The genetic algorithm is utilized to predict and diagnose [PERSON]'s disease, while the support vector machine is employed as a classification technique. The method exhibits a precision of 93.01%, a recall rate of 89.13%, and a feature detection rate of 96.80%. The field of utilizing photographs for the intelligent diagnosis of Alzheimer's disease has been subject to thorough investigation. The present investigation centers on the methodologies utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset as the primary input data. To identify [PERSON]'s disease, a versatile cognitive classification technique was used as an illustration [119]. The present investigation considers the implementation of magnetic resonance imaging (MRI) scans, the utilization of image processing methodologies, the execution of feature extraction procedures, and the classification of data through support vector machines. This study has considered various criteria, including accuracy, sensitivity, and rate of features. The findings indicate that the respective percentages for these criteria are 93.2%, 93.00%, and 93.3%. As reported in [120], a previous study initially examined a sequence of preprocessing procedures. The process of weighing involves two distinct operations: feature selection and extraction, followed by segmentation of the available samples. Subsequently, a deep learning neural network is employed for training and data testing, serving as the classification technique within the scope of this investigation. [PERSON]'s disease is anticipated, diagnosed, and subsequently assessed. The efficacy of this approach has been evaluated concerning the classification reliant on support vector machines, which has exhibited noteworthy outcomes. The research proposes a method referred to as SAE, which exhibits a high level of accuracy at 91.40%. Additionally, the method demonstrates a sensitivity rate of 92.32% and a feature rate of 90.42%. Notably, the SAE approach proves to be marginally more efficient than the support vector machine when utilizing deep learning. The subject matter of reference [121] pertains to using machine learning techniques to diagnose Alzheimer's disease. Alzheimer's is a progressive neurodegenerative disorder that impairs cognitive function, including memory. Regrettably, as of yet, there is no singular diagnostic test capable of identifying this ailment. Relying solely on cerebellar examinations cannot be deemed a definitive determinant in ascertaining the presence or absence of the condition. Based on social aptitude connections and a review of past clinical records, medical professionals have diagnosed an individual with Alzheimer's disease. The present model has the potential to be modified through the implementation of artificial intelligence and machine learning algorithms. Extensive processing is necessary due to data generated from diverse sources within intricate and evolving settings.