ALZHEIMER’S DETECTION AND CLASSIFICATION USING SVM

Machine Learning

Alzheimer's disease (AD) is the leading cause of dementia in older adults. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of Alzheimer's disease (AD) is more effective, and it causes fewer minor damage than a treatment done at a later stage. The paper proposes a technique based on Support Vector Machine that identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Magnetic resonance imaging (MRI) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML model. The proposed classification scheme can be used by doctors to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with this Machine Learning(ML) algorithm.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.