Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. In this paper, a methodology SMOTE-RF is proposed for AD prediction. Alzheimer’s is predicted using machine learning (ML) algorithms. Performance of three algorithms decision tree (DT), extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies (OASIS) longitudinal dataset available on Kaggle is used for experiments. Dataset is balanced using synthetic minority oversampling technique (SMOTE). Experiments are done on both imbalanced and balanced datasets. DT obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum 87.84% accuracy on the imbalanced dataset. DT obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. Maximum accuracy of 95.03% is achieved with SMOTE-RF