A Classification based approach for early stage Prediction and Multi-dimensional analysis of Alzheimer’s Disease

Alzheimer's disease (AD) presents significant challenges to global healthcare systems due to their rising incidence rates and profound impact on individuals and societies. Early prediction and intervention strategies are urgently needed to address thiue cognitive decline associated with AD, especially with an aging population. This research paper explores the integration of machine learning (ML) techniques to predict AD in its early stages. Various ML algorithms including Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, and Voting classifiers were employed and optimized using the Open Access Series of Imaging Studies (OASIS) dataset. Our models demonstrated commendable performance in AD prediction, achieving high Precision, Recall, Accuracy, and F1-score metrics. Beyond prediction, our research extended to disease progression forecasting, drug discovery, and image analysis, leveraging ML to unravel intricate disease dynamics and enhance diagnosis precision and treatment efficacy. The comprehensive framework outlined in this research facilitates accurate AD prediction and analysis, empowering clinicians to intervene earlier and mitigate the severe social, financial, and economic implications of AD. Adoption of our multifaceted classification scheme could transform healthcare, leading to more efficient disease management, improved patient outcomes, and reduced societal burdens associated with neurodegenerative diseases.