Improving Alzheimer’s classification using a modified Borda count voting method on dynamic ensemble classifiers

Alzheimer’s detection is a challenging task for physicians. There are subtle differences in the bio-marker characteristics of Alzheimers and mild cognitive impairment patients which is very difficult to detect by a physician. Machine learning approaches are widely used for predicting a patient as having either Alzheimer’s or mild cognitive impairment. For developing models that distinguish between Alzheimer’s and mild cognitive impairment patients, the researchers used a dynamic ensemble of classifier selection algorithms. These algorithms perform voting on ensemble classifiers without considering preferential choices of the Alzheimer’s and mild cognitive impairment categories. Thus, this paper applies a modified Borda count voting weightage method instead of the majority voting and Borda voting for classifying Alzheimer’s, healthy control, and mild cognitive impairment patients classification on dynamic ensemble of classifier selection algorithms. Six dynamic ensemble of classifier selection algorithms are used in the study. Ten pools of classifiers including random forest, bagged decision tree, extra trees, Adaboost, rotation forest, decision tree, bagged support vector machine, bagged multilayer perceptrons, majority voting ensemble, and stacking classifier are used as classifier input for the dynamic ensemble of classifiers. The results suggest that the application of the proposed method can improve the classification performance for Alzheimer’s, mild cognitive impairment, and healthy patients when compared to the traditional voting methods after applying most of the dynamic ensemble of classifier selection algorithms used in the study. The application of a modified Borda count voting method on the dynamic ensemble of classifiers resulted in an increase of balanced classification accuracy ranging from 1 to 9%. The highest balanced classification accuracy of 86% is reported when random forest is applied to meta-learning for dynamic ensemble selection algorithms with the proposed voting method. It is also noted that there is an maximum increase in balanced classification accuracy of 9% is observed when applying rotation forest on K-nearest output profiles classifier using the proposed modified Borda count voting method. Thus, the increase in the balanced classification accuracy after applying the proposed modified Borda count voting method can make a positive impact on high-stakes healthcare applications like Alzheimer’s detection.