Novel Method for Detection of Alzheimer’s Disease using Gini Impurity based Decision Tree Model

Computer aided diagnosis (CAD) tool has received reasonable attention of the researchers over the last few decades in identifying the occurrence and/or progression of several diseases. In this paper, machine learning based one such tool has been employed for possible classification of Alzheimer’s disease. Relevant features of brain parameters have been extracted and subsequently a decision tree is formed to classify the data points. Proposed algorithm is designed and validated on a good number of training and testing data. Experimental results have shown to have a classification accuracy of 95%, sensitivity of 96.7% and specmcity of 93.3%.