Prediction of Alzheimer’s disease using oasis dataset

© BEIESP. Alzheimer’s Disease (AD) is hard to predict in the early stage. But giving treatment at an early stage of AD is more effective and causes less damage to people. Various approaches like Random Forest, Support Vector Machine, Gradient Boosting and Lasso Regression have been applied to identify the best parameters for the Alzheimer’s Disease prediction. Accuracy results are tabulated. Alzheimer’s Disease has been predicted using Open Access Series of Imaging Studies (OASIS) dataset. Random Forest has the best accuracy rate of 97.94% and SVM has the least accuracy rate of 93.6%.