EARLY STAGE OF ALZHEIMER’S DISEASE PREDICTION

Alzheimer’s disease is a neurological ailment that progresses over time. Early identification is essential for effective treatments. An overview of current studies in the area of early-stage Alzheimer’s disease prediction is provided in this abstract. Our research examines a complex strategy that integrates information from several data sources, such as neuroimaging, cognitive tests, genetic markers, and clinical history. Machine learning algorithms are used to analyse these many data sets in an effort to spot unnoticeable trends and biomarkers indicating the start of the disease. We emphasise the significance of extensive longitudinal studies, which contribute to the creation of prediction models and offer insightful information about the course of Alzheimer’s disease. We also talk about the necessity for strict data privacy laws and the ethical issues surrounding the usage of sensitive health data. Early diagnosis of Alzheimer’s disease is crucial for prompt interventions, individualised treatment regimens, and raising the standard of living for those who are affected. This abstract provides a look into the exciting lines of inquiry in this crucial domain that have the potential to transform early detection and treatment approaches for Alzheimer’s disease.