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.

本文提出了一种基于游戏的患者特定的迁移方法（PSGT）来预测帕金森病的严重程度。该方法考虑到了患者之间的异质性，并采用实例迁移的方式来弥补患者样本数据较少的问题。通过实验验证，发现 PSGT 方法相较于其他方法具有更好的预测效果和稳定性。