Prediction Of Alzheimer Patients with Machine Learning Algorithms

Abstract

Alzheimer's disease is a neurological illness that impacts mental and emotional functions functions, has become a global concern due to its increasing prevalence. While age is the primary risk factor, other factors such as the APOE ε4 gene, family history, and brain injury also play a role. To date, there is no effective treatment for Alzheimer's, making early detection crucial. This study aims to explore early detection methods for Alzheimer's using machine learning algorithms, including transformer techniques. The results indicate that the Random Forest algorithm with Transformer methods achieved the highest accuracy of 98.9%. These findings are expected to contribute to the development of more accurate and efficient early detection strategies and improve the management of developing Alzheimer's later on.