Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

Abstract

Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy of the algorithms is 67.68%, 84.58%, 87.42%, and 88.90% respectively. The validation applied is 10-fold cross-validation. Then, a confusion matrix is generated and class-wise performance is analysed to find the best algorithm. The ADNI database is used for the implementation process. To compare the performance of the proposed model, the OASIS dataset is applied to the model with the same algorithms and the accuracy of the algorithms is 98%, 99%, 99% and 100% respectively. Also, the time for the model construction is compared for both datasets. The proposed work is compared with existing studies to check the efficiency of the proposed model.