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.
翻译:Dysphonia是帕金森病(PD)早期症状之一。 多数现有方法都采用特有选择方法,为所有PD病人寻找最佳的语音特征子集。 很少有人考虑病人之间的异质性,这意味着需要为不同的病人提供具体的预测模型。 然而, 建立具体模型面临着样本规模小的挑战, 这使得它缺乏概括性能力。 例转移是解决这一问题的有效方法。 因此,本文件建议采用基于特定病人的游戏转移方法来进行PD严重程度预测。 首先, 使用一种选择机制从源域选择具有类似病情趋势的PD病人到目标病人,这大大降低了负转移的风险。 然后, 转移对象及其病例对目标疾病估计的贡献,由沙普利值进行公正的评估,这提高了该方法的可解释性。 其次, 确定在转移对象中有效案例的比例,将贡献较大的案例转移到进一步缩小转介的子和目标对象与目标对象之间的差异。 最后, 所选选的PGEV的子类比, 用于更精确性能评估。 将更精确性数据添加到PGES的模型, 用于更精确性测算。