Using decision tree classification to assist in the prediction of Alzheimer's disease

Abstract

Alzheimer's disease is one of the most common forms of dementia affecting millions of senior people worldwide. In this paper, we develop an Alzheimer's disease prediction model that can assist medical professionals in predicting the status of the disease based on medical data about patients. The sample medical data we use has five important attributes, namely, gender, age, genetic causes, brain injury, and vascular disease. The sample also contains values for seventeen different patients that represent seventeen medical cases. We perform decision tree induction to create a decision tree that corresponds to the sample data. We base our selection of nodes in the tree on the Entropy or Information Gain computed for each attribute. At each level of the tree, the right attribute is chosen as a splitting attribute if it gives us the highest Information Gain.