PREDICTION AND DIAGNOSIS OF ALZHEIMER’S USING MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.47820/recima21.v6i5.6399Keywords:
Machine Learning. Alzheimer Disease. Classification. Supervised Machine Learning.Abstract
This study aims to explore the use of machine learning techniques to predict the diagnosis of Alzheimer’s disease, a neurodegenerative condition that is challenging to detect early. The study employs techniques such as Support Vector Machine, Random Forest, and K-Nearest Neighbors, applying them to a dataset containing demographic, lifestyle, and medical history information from patients. The results enabled the evaluation of model performance using metrics such as accuracy, precision, recall, and specificity.
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