ABSTRACT
The field of computerized healthcare has expanded quickly, and it should be appreciated in advance for the development of medical imaging and machine learning technologies in the field of healthcare. One of the kinds of Dementia is Alzheimer's disease which affects behavior, memory, thinking. When symptoms worsen, they interfere with our everyday activities. It is found in people of the age 50. The paper's goal is to conduct a comparative study of four methodologies for detection and diagnosis of AD in the early stage: Random Forest, XGBoost, Support vector Machine (SVM) and Graphical Neural Networks (GNNs) . Each method is evaluated using a flexible dataset and the performance of the method is assessed based on key metrices including recall, accuracy, precision F1-score, and area under the curve (AUC). Our results in the paper highlight the benefits and drawbacks of each strategy, offering guidance for further study and practical applications.
