<![CDATA[Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. It is characterized by deterioration of memory, language, problem-solving, and other cognitive skills that affect a person's ability to perform everyday activities. This decrease occurs because nerve cells (neurons) in parts of the brain involved in cognitive function are damaged and stop working properly. One way to detect Alzheimer’s is to use models of machine learning algorithms. In this study, the authors' team aimed to compare models of machine learning algorithms to find the one that gives better results in prediction Alzheimer's disease. Machine learning models algorithms in this study were built using Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy rate.]]>
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