Early Detection of Alzheimer’s Disease Using Advanced Machine Learning Techniques: A Comprehensive Review

Alzheimer’s disease (AD) is a slow-paced irreversible brain disease and a neurodegenerative disorder that accounts for approximately 70% of the dementia cases estimated worldwide, the number of which totals to more than 46 million. AD affects the brain's thinking capacities along with significant memory loss. People with onset of aging are found to be more prone, with greater memory loss, cognitive difficulties, etc. Currently, there exists no fixed cure for AD, but early detection and characterization are proven to be helpful. Methodologies like electroencephalograms (EEG), magnetic resonance imaging (MRI), computed tomography, positron emission tomography (PET) scan, etc., are helpful in providing information regarding the persisting conditions of the brain cells. Computer-aided diagnosis (CAD) along with biomedical data processing when applied to machine learning and deep learning methodologies has vastly helped sophisticated techniques like CNNs, SVMs, etc., to evolve and achieve promising prediction accuracies. This paper provides a review and critical evaluation of recent research on early detection of AD using ML techniques which employ a variety of complex optimization and statistical techniques to obtain a better accuracy score. Along with advancement in computational capabilities, other factors such as preprocessing and feature extraction along with class imbalance have distinctively helped improve the prediction score which has overall helped produce better prediction with respect to earlier detection of AD.