A Comparison of Machine Learning Algorithms for Alzheimer’s Disease Prediction

Abstract

Lately, Alzheimer’s disease has emerged as a big worry. Approximately 45 million individuals are afflicted with this illness. Alzheimer’s is a degenerative brain disease that mostly affects the elderly and has an unclear aetiology and pathophysiology. Dementia is the primary cause of Alzheimer’s disease, as it gradually affects brain cells. This sickness caused people to lose their capacity to read, think, and many other skills. By forecasting the illness, a machine learning system can lessen this issue. The primary goal is to identify dementia in a range of people. This research discusses the findings and analysis related to the identification of dementia using several machine learning models. The method has been developed using the Open Access Series of Imaging Studies (OASIS) dataset. Despite the dataset’s limited size, some significant values are present. Many machine learning models have been applied and the dataset examined. For prediction, decision trees, random forests, logistic regression, and support vector machines have all been employed. The system has been used both with and without fine-tuning. When the results are compared, it is discovered that the support vector machine produces the best outcomes of all the models. Among a large number of patients, it had the highest accuracy in identifying dementia. The technique is easy to use and can identify individuals who may be suffering from dementia.