SVM-Based Classifier For Early Detection Of Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is a disorder affecting the brain and its functions that results in permanent damage to the brain. This multifaceted disease slowly destroys brain cells, reducing a person's ability to think, remember, and carry out even the most basic duties. Ultimately, this cognitive decline leads to dementia. A significant portion of the global population faces metabolic challenges like Alzheimer's disease and diabetes. Recent research has explored various machine learning approaches aimed at early disease detection. Early AD diagnosis is very important for a speedy recovery and minimizing damage to brain cells. In this proposed work, a machine learning model is developed using Support Vector Machine to detect individuals with dementia (AD) or without dementia (NC). Our model is trained using 2D Magnetic Resonance Imaging (MRI) brain scan images. We computed common performance measures like the F1-score and accuracy to assess the model. The 15-fold cross-validation technique was applied to cross-validate these results. Notably, our results demonstrate that Support Vector Machine (SVM) attained a remarkable accuracy of 99.06% in the detection of AD. Accurate Alzheimer's disease detection through machine learning algorithms can greatly reduce the annual mortality rates related to AD.