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 gradi-ent 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 com-pared. 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 pre-diction 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 pro-posed 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 informa-tion fusion ensembles for other medical and non-medical problems.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a novel ensemble learning framework for predicting Alzheimer's disease progression. The framework incorporates heterogeneous base learners into an integrated model and utilizes multimodal time-series data. The proposed model achieves outstanding performance in predicting disease progression and can be implemented in low-cost healthcare environments. The balance between accuracy and diversity is found to be critical in selecting ensemble members. The proposed framework holds promise for efficient information fusion ensembles in medical and non-medical problems.