Classification of Parkinson's disease based on multi-modal features and stacking ensemble learning

Background: Early diagnosis of Parkinson's disease (PD) enables timely treatment of patients and helps control the course of the disease. An efficient and reliable approach is therefore needed to develop for improving the clinical ability to diagnose this disease. New Method: We proposed a two-layer stacking ensemble learning framework with fusing multi-modal features in this study, for accurately identifying early PD with healthy control (HC). To begin with, we investigated relative importance of multi-modal neuroimaging (T1 weighted image (T1WI), diffusion tensor imaging (DTI)) and early clinical assessment to classify PD and HC. Next, a two-layer stacking ensemble framework was proposed: at the first layer, we evaluated advantages of these four base classifiers: support vector machine (SVM), random forests (RF), K-nearest neighbor (KNN) and artificial neural network (ANN); at the second layer, a logistic regression (LR) classifier was applied to classify PD. The performance of the proposed model was evaluated by comparing with traditional ensemble models. Results: The proposed method performed an accuracy of 96.88 %, a precision of 100 %, a recall of 95 % and a F-1 score of 97.44 % respectively for identifying PD and HC. Comparison with Existing Method: The classification results showed that the proposed model achieved a superior performance in comparison with traditional ensemble models. Conclusion: The stacking ensemble model with efficiently and effectively integrate multiple base classifiers performed higher accuracy than each single traditional model. The method developed in this study provided a novel strategy to enhance the accuracy of diagnosis and early detection of PD.