Detecting early-stage Alzheimer’s disease with Machine Learning algorithms

Abstract: Alzheimer’s disease (AD) accounts for the majority of all cases of dementia and can be characterized as a disease that causes a progressive decline of cognitive functions. Detecting the disease at it’s earliest stage is important as medical treatments can be more effective if they can be applied before the disease has caused irreparable brain damage. However, making a correct diagnosis of AD can be difficult, especially in the early stage when the symptoms are still mild. Machine learning algorithms can help in this process, with the purpose of this study being to investigate just how accurately machine learning algorithms can detect early-stage AD. Three algorithms were selected for the study, Random Forest, AdaBoost and Logistic Regression, which were then evaluated on the accuracy of their predictions. The results showed that Random Forest had the best overall performance with an accuracy of 79.78%. AdaBoost attained an accuracy of 76.40% and Logistic Regression attained an accuracy of 74.16%. These results suggest that machine learning algorithms can be used to make relatively accurate predictions of AD even when the disease is in it’s early stage.