Late-Life Alzheimer's Disease (AD) Detection Using Pruned Decision Trees

Abstract

Machine Learning (ML) is a contemporary technique of artificial intelligence. These methods are exponentially rising in the medical field, especially in diagnosis and disease predictions. The present study was aimed to develop a decision tree model to predict late-life Alzheimer's disease (AD). A dataset of 150 subjects along with 373 MRI sessions demographic values were considered in this paper. Pruned decision trees (J48) were employed to do predictive analysis on AD subjects. Model validation was conducted with cross fold (k = 10) methods. Performance measures were evaluated by accuracy, precision, and receiver operating characteristic (ROC) curve. Results were provided an accuracy of 88.7%, precision of 86.7%, and ROC of 91.8% was recorded.