Analysis of Classification & Feature Selection Techniques for Detecting Dementia

Abstract

Dementia is a brain disorder emerging as a global health problem in adults of age 65 years or above. A lot of studies are going on for the early detection of dementia using various machine learning algorithms. In this study, we have implemented four machine learning algorithms as classifiers namely, Naïve Bayes, Random Forest, Multilayer Perceptron and SMO in combination with various feature selection techniques to obtain the classification accuracy as a measure for the detection of dementia. It is found that Random Forest is showing best result of 98.6% when CFSSubsetEval Feature selection technique is used.