Remote Parkinson's disease severity prediction based on causal game feature selection

Abstract

Telemonitoring of Parkinson's disease has important implications for early diagnosis and treatment of patients. Most of the existing feature selection methods for remote prediction of PD severity are based on correlation and rarely consider causality, thus compromising the robustness of the model. Therefore, a causal game-based feature selection (CGFS) model is proposed for remote PD symptom severity assessment. Firstly, to address the challenge of small data size, the similar patient transfer strategy is designed to find data from source domain patients with conditions similar to those of the target patient. Secondly, the undirected equivalent greedy search method is employed to construct the causal graph between features and PD severity scores, and the robustness of the model is improved by selecting causal features. Then, to enhance the prediction accuracy, this paper utilizes the cooperative game approach Shapley value to evaluate the contribution of neighborhood nodes of the target value, and selects the features with causality and high contribution to form the final feature subset. Finally, the subset is input into the random forest to further enhance robustness and performance of the model. Experiments on Parkinson’s telemonitoring dataset and the tapping dataset with different biomarkers show that the robustness of the feature subset selected by the CGFS model, and the prediction performance is better than advanced models compared. Therefore, the validity and universality of the CGFS method is demonstrated in remote PD severity prediction.