Fatema Nafa, Evelyn RodriguezArgueta, Annie Dequit and Changqing Chen, Salem State University, USA

Alzheimer's disease (AD), a kind of dementia, is marked by progressive cognitive and behavioural problems that appear in middle or late life. Alzheimer's disease must be detected early in order to create more effective therapies. Dr. Alois Alzheimer was the first doctor in the medical field to notice an unusual state of change in the brains of his deceased patients with mental illness, which marked the start of Alzheimer's study. Machine learning (ML) techniques nowadays employ a variety of probabilistic and optimization strategies to allow computers to learn from vast and complex datasets. Because of the limited number of labelled data and the prevalence of outliers in the current datasets, accurate dementia prediction is extremely difficult. In this research, we propose a sustainable framework for dementia prediction based on ML techniques such as Support Vector Machine, Decision Tree, AdaBoost, Random Forest, and XGmodel. All the experiments, in this literature, were conducted under the same experimental conditions using the longitudinal MRI Dataset.

Machine learning, Alzheimer’s disease, Feature selection, Biomechanical parameters.