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@article{185802,
        author = {Mrs. Aparna T. Kulkarni and Dr. Anirudh K. Mangore},
        title = {Alzheimer's disease Prediction Using Machine Learning Techniques and Feature Selection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {},
        pages = {1-7},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185802},
        abstract = {One of the main reasons why older adults develop dementia is Alzheimer's disease (AD). Additionally, a sizable section of the global population has metabolic issues like diabetes and Alzheimer's disease. Degenerative effects of Alzheimer's disease are shown in the brain. This disease can lead to more people being inactive when the number of senior people increases since it affects their physical and mental abilities. This could affect their family members as well as the social, financial, and economic domains. Recently, researchers have looked into several deep learning and machine learning techniques to identify these illnesses early. Successful and minimally harmful recovery from AD is facilitated by early diagnosis and therapy. In order to predict Alzheimer's illness, this study suggests a machine learning model that combines GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost. The open access series of imaging studies (OASIS) dataset is used to train the model and assess its performance in terms of F1 score, recall, accuracy, and precision. Our results demonstrated that, for the AD dataset, the voting classifier achieved the greatest validation accuracy of 96%. Therefore, with precise identification, ML algorithms have the potential to significantly reduce the annual mortality rates from Alzheimer's disease.},
        keywords = {Alzheimer's disease (AD), Predict, Machine Learning, OASIS, etc},
        month = {October},
        }