Early detection of Alzheimer’s disease based on machine learning scenarios

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that is irreversible and insidious. Contemporarily, there is a very high incidence in the elderly and the causality of the disease remains unclear. The existing medications can only relieve the symptoms but cannot cure them completely. At present, the state-of-art machine learning approaches have been widely used in neurological diseases. This paper proposes machine learning approach to predict whether people will develop AD at an early stage based on the data from OASIS. By comparing the prediction results of four machine learning methods, the BP Neural Network had the best performance, whose accuracy rate reaches 86%. In terms of the state-of-art model to diagnose AD, it could lead to a reduction in the use of medical equipment and interventions at an earlier stage. Overall, these results shed light on broadening the implementation of machine learning techniques in medicine.