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.

中文翻译：

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使用自组织图和神经模糊集成预测帕金森病的 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）算法广泛用于通过预测统一帕金森病评定量表（UPDRS）的结果来诊断和跟踪这种疾病的进展，这种疾病会导致帕金森病的早期症状。在本研究中，我们的目标是开发一种基于两种方法集成的方法，一种互补，另一种方法是自组织映射和神经模糊集成，以及一种无监督学习算法。所提出的方法依靠对初始读数的分析所产生的变量的较高影响来获得正确且准确的初步预测。我们在 PD 数据集（包括语音提示）上评估了开发的方法。该过程通过均方根误差 (RMSE) 和修正 R 平方（修正 R2）进行评估。我们的研究结果表明，所提出的方法可以有效地通过语音信号（声音嘶哑的测量）组合来预测 UPDRS 结果。评估期间的初步结果显示数字证明了该方法的价值，例如预测过程中的 UPDRS = 0.955 和 RMSE 约为 0.2769。