International Journal of Science, Engineering and TechnologyAuthors: Maheswari R, Dr Sajana T, Uma Maheswari G
Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function and quality of life among aging populations worldwide. Early prediction and diagnosis of Alzheimer’s disease are critical for timely intervention and effective disease management. However, the increasing volume and complexity of healthcare data, including neuroimaging, clinical records, and genetic information, present significant challenges for conventional machine learning approaches. This study proposes a scalable framework that integrates deep learning algorithms with Apache Spark to enable efficient large-scale healthcare data analytics for Alzheimer’s disease prediction. Apache Spark is employed for distributed data preprocessing, feature engineering, and large-scale data management, while deep learning models are utilized to learn complex patterns associated with disease progression. Experimental evaluation demonstrates that the proposed framework achieves high predictive performance while significantly reducing computational overhead compared with traditional approaches. The findings highlight the potential of combining distributed computing and deep learning technologies for scalable and accurate Alzheimer’s disease prediction in modern healthcare environments.
DOI: http://doi.org/10.5281/zenodo.20569845
