Forecasting alzheimer's disease in its early stages with the use of machine learning algorithms
Department of Computer Science and Engineering, Rajeev Institute of Technology, Hassan.
Research Article
Global Journal of Engineering and Technology Advances, 2024, 20(01), 231–241.
Article DOI: 10.30574/gjeta.2024.20.1.0135
Publication history:
Received on 15 June 2024; revised on 28 July 2024; accepted on 31 July 2024
Abstract:
When it comes to dementia in the elderly, Alzheimer's disease (AD) takes the cake. Metabolic disorders impact a huge portion of the global population; as a result, there is a growing interest in using machine learning to better understand these conditions. The annual increase in their incidence rates is quite concerning. Neurodegenerative alterations impact the brain in Alzheimer's disease. Diseases impacting memory and functioning will affect healthcare providers, patients, and families at an increasing rate as the population ages. The monetary, social, and economic spheres will all feel these impacts to a significant degree. It is difficult to foretell the onset of Alzheimer's disease in its early stages. Compared to treatments administered later in the disease's progression, those administered early in the disease's progression are more effective and have less mild side effects. Finding the optimal parameters for Alzheimer's disease prediction has involved the use of several techniques, including Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers. Parameters such as F1-score for ML models, Precision, Recall, and Accuracy are used to evaluate the performance of models that use the Open Access Series of Imaging Studies (OASIS) data to make predictions about Alzheimer's disease. Medical professionals can use the suggested categorization system to identify these illnesses. Reducing yearly mortality rates of Alzheimer's disease through early diagnosis using these ML algorithms is tremendously advantageous. When tested using AD test data, the suggested approach outperforms the competition with an average validation accuracy of more than 90%. In contrast to previous studies, this one achieved a far higher test accuracy score.
Keywords:
Alzheimer's disease (AD); Machine Learning; Voting Classifier; OASIS data; Chi-Square; Mortality rates
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0