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@article{202379,
        author = {P.Rama chandra reddy and P.Vamsi Kumar Reddy and R,Bhanu prathap reddy and G.Mahalakshmi},
        title = {Dual-Modality Alzheimer's Disease Prediction System Using Clinical Data and MRI Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7077-7084},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202379},
        abstract = {Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that requires an accurate and comprehensive diagnosis at the earliest possible stage in order to effectively treat it. The dual-modality prediction system described in this project combines two separate modules; one for clinical evaluations and the other for MRI evaluations. The clinical component uses a Random Forest classifier and takes into account the demographic information, MMSE scores, and past diagnosis of the person being evaluated. The MRI module uses the same data; however, instead of utilizing a classifier, it analyzes the pattern of brain activity directly from the brain using MRI. As an additional benefit to the combination of the two modules, the project also provided a web application to give patients, clinicians and administrators secure access to the computerized prediction system and a means to input clinical data, upload MRI scans and receive real time predictions. Together, the combination of both modalities will provide a more accurate and reliable prediction and therefore closely resemble the diagnostic processes used in clinical practice. In conclusion, the project illustrates how machine learning can be applied to the health care system, allowing for earlier detection, remote monitoring and integrated analyses of patients with Alzheimer's Disease.},
        keywords = {Alzheimer's disease, dual-modality prediction, clinical data analysis, MRI image processing, machine learning, Random Forest, pattern-based classification, web-based healthcare system, early detection, medical imaging.},
        month = {May},
        }