A Novel & Effective Detection of Alzheimer's Disease using Extremely Randomized Trees Models

Abstract

Alzheimer's disease (AD) is a chronic and irreversible brain disease without adequate treatment. However, currently, available drugs can slow the progression of the condition. As a result, detecting Alzheimer's disease early is essential to avoiding and limiting the disease's progression. The primary purpose of this research is to establish a comprehensive framework for the early identification of Alzheimer's disease and the medical classification of the disease's various stages. Alzheimer's disease is classified into two stages. The proposed work employed multiple machine learning (ML) approaches such as Gradient boost (GB), Decision Tree (DT), Support Vector Machine (SVM), and Extra tree algorithm (ETA) to diagnose and classify Alzheimer's disease earlier using the Open Access Series of Imaging Studies (OASIS) dataset, with the ETA classifier achieving significant performance and result. The ETA classifier, in particular, outperformed the others regarding total classification performance. The proposed ETA achieves an accuracy of 0.88, precision of 0.93, recall of 0.85, and f1-score of 0.89. The machine learning (ML) technique that we have selected to apply for Alzheimer's disease detection is the Extremely Randomised Trees (extra trees) algorithm. This was a conscious decision on our side. We may classify AD with high accuracy using machine learning methods.