Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction

TL;DR

This paper introduces a patient-specific transfer learning approach using game theory to improve Parkinson's disease severity prediction from voice features, addressing patient heterogeneity and small sample challenges.

Contribution

It proposes a novel game-based transfer method that selects similar patients and evaluates instance contributions to enhance prediction accuracy and interpretability.

Findings

Outperforms existing methods in prediction accuracy.

Reduces negative transfer through patient similarity selection.

Improves model stability across different patients.

Abstract

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.