Early Alzheimer’s Detection Using Random Forest Algorithm

Abstract

Alzheimer’s disease (AD) is a progressive neurological ailment causing damage to brain cells. Beginning with mild symptoms that usually goes unnoticed, the disorder gets worse as it progresses hindering the general abilities of person. Early AD symptoms being ordinarily simple, detection occurs only on disease progression to an advance irreversible stage. Early detection of AD is thus critical to reduce the adverse effects of the disease. Earlier detection can prove promising for the development of specific treatment strategies that improve or slow AD progression. Machine Learning (ML) approach has become increasingly useful in the detection of Alzheimer’s disease in recent years. In this paper, early detection of Alzheimer’s disease using different machine learning algorithms for predictive categorization of patients is presented. The study suggests that random forest algorithm offers best performance for early prediction of Alzheimer’s disease with an accuracy of 93.69%. A GUI for users to enter parameters for early detection and display the categorized result for random forest algorithm is also designed.