Early Detection of Alzheimer's Disease Using Cognitive Features: A Voting-Based Ensemble Machine Learning Approach

Abstract

Early detection of Alzheimer's disease (AD) is vital for adequate control. Machine learning techniques have gained much attraction due to their efficiency in predicting AD using cognitive tests. Ensemble machine learning models are helpful in improving the robustness of the learning system via combining multiple machine learning models. This article proposes a novel ensemble machine learning technique for the early detection of AD. First, a novel feature selection technique referred to as Neighborhood Component Analysis and Correlation-based Filtration (NCA-F) is proposed to select the vital cognitive features from a given dataset. Second, various machine learning classifiers were trained using the proposed NCA-F method. The top classifiers were selected for voting based on the performance results. The voting is performed using an adaptive weight matrix process. The output label of a model is multiplied by the F1 score and represented as weight. The results revealed an accuracy of 93.92% when using adaptive voting, which is better than the accuracy of 90.53% observed when using the traditional artificial neural network method. The proposed technique improved the accuracy of detecting AD at an early stage. Furthermore, the results against a recent study using same features also revealed an improvement of 12.12% in accuracy.