Predicting UPDRS in Parkinson’s disease using ensembles of self-organizing map and neuro-fuzzy

Siren Zhao, Jilun Zhang, Jianbin Zhang
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Abstract

Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process.
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使用自组织图和神经模糊集合预测帕金森病的 UPDRS
帕金森病(PD)是一种复杂的退行性疾病,会影响负责身体运动的神经细胞。人工智能(AI)算法通过预测统一帕金森病评分量表(UPDRS)的结果,被广泛用于诊断和跟踪这种疾病的进展情况,这种疾病在早期阶段会导致帕金森病症状。在这项研究中,我们的目标是开发一种基于自组织图集合和神经模糊两种方法互补整合的方法,以及一种无监督学习算法。所提出的方法依赖于对初始读数分析所产生的变量的更高效应,以获得正确、准确的初步预测。我们在包含语音线索的 PD 数据集上对所开发的方法进行了评估。我们用均方根误差(RMSE)和修正 R 平方(修正 R2)对这一过程进行了评估。我们的研究结果表明,所提出的方法能有效地通过语音信号(声音嘶哑的测量指标)组合预测 UPDRS 的结果。评估过程中的初步结果显示的数字证明了所提方法的价值,如在预测过程中,UPDRS = 0.955,RMSE 约为 0.2769。
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