A Review of Recent Advances in Alzheimer's Disease Machine Learning Algorithms for Early Mild Cognitive Impairment Prediction

Abstract

Research into early Alzheimer's disease (AD) is the main focus of clinical investigations. Clinical progression predictions from normal to moderate cognitive impairment (MCI), MCI to dementia, AD, or non-progression are not very accurate. Medication utilization is decreased and trial efficiency is increased with accurate symptomatic progressor identification. Preparing for Alzheimer's therapy would thus be easier with an early diagnosis. As a result, the disease could develop more slowly. Alzheimer's may be recognized using machine learning algorithms. The performance categorization of patients with Alzheimer's disease may be improved using advanced machine learning. As a result, this research builds upon previous diagnostic studies of Alzheimer's disease conducted since 2016. Participant nation, data modalities and characteristics, feature extraction techniques, number of follow-up data points, anticipated time from mild cognitive impairment to Alzheimer's disease, and machine learning models are all taken into account in this overview of studies on Alzheimer's detection. The characteristics and machine learning models used in earlier Alzheimer's research may be explained to novice researchers by this review. Because it is structured to adhere to the many elements of the Machine Learning technique, this study aids researchers in objectively assessing the literature on Alzheimer's detection. learning models used in earlier Alzheimer's research may be explained to novice researchers by this review. Because it is structured to adhere to the many elements of the Machine Learning technique, this study aids researchers in objectively assessing the literature on Alzheimer's detection.