Developing ML model for early detection and prediction of Alzheimer's disease using multi modal biomarkers

The early identification and outlook of Alzheimer's disease (AD) are the primary focus of this review of the literature. It emphasizes the significance of early detection, the application of machine learning approaches for AD prediction, and the utilization of multimodal biomarkers from several data sources. The review also covers resolving flaws in AD prediction algorithms, privacy and ethical issues, and the usability of machine learning models. A synopsis of the state of the field's research and the significance of interdisciplinary cooperation in advancing machine learning and multimodal biomarkers for early AD detection are included in the conclusion. For the benefit of the millions of people afflicted by this illness, the primary goal is to make early AD identification a clinical reality.