Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers

Predicting Alzheimer's disease (AD) progression is crucial for improving the management of this chronic disease. Usually, data from AD patients are multimodal and time series in nature. This study proposes a novel ensemble learning framework for AD progression incorporating heterogeneous base learners into an integrated model using the stacking technique. This framework is used to build a 4-class ensemble classifier, which predicts AD progression 2.5 years in the future based on the multimodal time-series data. Statistical measures have been extracted from the longitudinal data to be used by the conventional machine learning models. The examined ensemble members include k-nearest neighbor, extreme gradient boosting, support vector machine, random forest, decision tree, and multilayer perceptron. We utilize three time-series modalities and one static non-time series modality of 1371 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) to validate our model. Several homogeneous and heterogeneous combinations of ensemble members were implemented, and their performance compared. The balance between accuracy and diversity when selecting ensemble members was investigated. We found that both accuracy and diversity are equally critical metrics to obtain an optimal ensemble model. Furthermore, our testing showed that the proposed model achieves outstanding progression prediction performance. The proposed model achieved a high performance without using neuroimaging data, which means that the model could be implemented in low-cost healthcare environments. The proposed model has achieved superior results compared with the state-of-the-art techniques in Alzheimer's and ensemble classifiers domains. The proposed framework can be used to implement efficient information fusion ensembles for other medical and non-medical problems.