Alzheimer's classification using dynamic ensemble of classifiers selection algorithms: A performance analysis

Abstract Alzheimer's is a type of severe cognitive impairment where an individual cannot do their daily day-to-day activities. It is a challenging task to find out the Alzheimer's and Mild Cognitive Impairment patients. This study aims to compare the performance of the state of the art Dynamic Ensemble Selection of Classifier algorithms for classifying healthy, Mild Cognitive Impairment, and Alzheimer's disease participants at the baseline stage itself using multimodal features. The data used in the study is from Alzheimer's Disease Neuroimaging Initiative-TADPOLE dataset. The medical imaging, Cerebro-spinal fluid, cognitive test, and demographics data of the patients at the baseline visits are considered for the prediction purpose. The performance of the state-of-the-art Dynamic Ensemble of Classifier Selection algorithms is compared using these features in terms of Balanced Classification Accuracy, Sensitivity, and Specificity. The most commonly used pool of Machine Learning classifiers is used as the input for Dynamic Ensemble of Classifier Selection algorithms. Moreover, the performance of the pool of Machine Learning classifiers without using the Dynamic Ensemble Selection of Classifiers algorithms are also compared. The performance metrics such as Balanced Classification Accuracy, Sensitivity, and Specificity are increased after using the Dynamic Ensemble of Classifier Selection algorithms on most of the pool of classifiers for classifying healthy, Alzheimer's, and Mild Cognitive Impairment patients is promising.