Review and Comparison for Alzheimer's Disease Detection with Machine Learning Techniques

Abstract

Alzheimer's disease is an advanced neurological condition marked by cognitive deterioration and memory loss. Early AD identification is crucial for quick treatment and better patient results. The diagnostic process can now be automated thanks to machine learning techniques, which can offer precise and effective tools for detecting early Alzheimer's disease. This study suggests an innovative method for detecting Alzheimer's disease that combines feature extraction, dimensionality reduction, and classification algorithms. The study makes use of a sizable dataset made up of neuroimaging scans, clinical evaluations, and demographic data from a wide range of people, including both people with confirmed diagnoses of Alzheimer's Disease and healthy controls. The study also investigates how various feature sets and mixtures of imaging modalities affect classification ability. In order to strengthen the robustness and generalization capacity of the Alzheimer's Disease detection model, an ensemble learning strategy is also being looked into.

An independent test set is used to validate the suggested approach, which is then thoroughly assessed using a cross-validation framework. The outcomes show how well the suggested method performs in precisely differentiating between those with Alzheimer's disease and healthy controls. Additionally, the model's performance is contrasted with other cutting-edge techniques for detecting Alzheimer's disease, emphasizing its competitive advantage in terms of precision and computational effectiveness. Algorithms for supervised classification are used in the suggested method. Classifiers such as Random Forests, Support Vector Machines and Navie base are used in a comparative analysis.