Early detection of Alzheimer's Disease (AD), i.e. before symptom onset, would provide the opportunity for development and testing of interventions at earlier stages, when the disease process may still be altered or interrupted. Computer algorithms combining machine learning with non-invasive imaging and other biomarkers for AD have been developed in an effort to improve early detection methods. However, so far, none of the individual algorithms perform at a level that qualifies for clinical use. In this study, we investigated whether combining several existing AD prediction algorithms improves performance and generalisability.
State-of-the-art AD progression prediction algorithms were collected from the TADPOLE-SHARE project. Algorithms were trained on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and made forecasts of the clinical diagnosis (CN, MCI, or AD). These algorithms were combined using i) simple, unlearned fuser methods and ii) learned fuser methods. In total, seven experiments were conducted, exploring different combination strategies with increasing complexity of fusers. Finally, we implemented and added our own individual algorithm, a residual neural network (ResNet). All individual algorithms and ensembles were evaluated with the multiclass area under the curve (mAUC) and the balanced classification accuracy (BCA) performance metrics. Statistical significance was evaluated with the McNemar test.
Results. TADPOLE-SHARE resulted in the collection of eight algorithms, from which five were reused for combination. Overall, combining algorithms slightly improves performance (i.e. increased BCA and mAUC), although improvements were not statistically significant (McNemar test). Both BCA and mAUC showed a trend of improved performance with increasing fuser complexity i.e. data learned fusers and re-entering original data features. DoubleResNet was the best performing ensemble (BCA = 0.809 [±0.026], mAUC = 0.902 [±0.020]) and performed slightly better than the best scoring fused algorithm EMCEB (BCA = 0.761 [±0.029]; mAUC = 0.866 [±0.020]).
These preliminary results suggest that combining pre-existing AD progression prediction algorithms might provide the increase in performance and generalisability needed to enable clinical translation. To do so, future work should be focused on increasing the interoperability of currently existing and newly developed algorithms.