In this study, a new instance selection method that combines the neural network with weighted fuzzy memberships (NEWFM) and Takagi–Sugeno (T–S) fuzzy model was proposed to improve the classification accuracy of healthy people and Parkinson’s disease (PD) patients. In order to evaluate the proposed instance selection for the classification accuracy of healthy people and PD patients, foot pressure data were collected from healthy people and PD patients as experimental data. This study uses wavelet transforms (WTs) to remove the noise from the foot pressure data in preprocessing step. The proposed instance selection method is an algorithm that selects instances using both weighted mean defuzzification (WMD) in the T–S fuzzy model and the confidence interval of a normal distribution used in statistics. The classification accuracy was compared before and after instance selection was applied to prove the superiority of instance selection. Classification accuracy before and after instance selection was 77.33% and 78.19%, respectively. The classification accuracy after instance selection exhibited a higher classification accuracy than that before instance selection by 0.86%. Further, McNemar’s test, which is used in statistics, was employed to show the difference in classification accuracy before and after instance selection was applied. The results of the McNemar’s test revealed that the probability of significance was smaller than 0.05, which reaffirmed that the classification accuracy was better when instance selection was applied than when instance selection was not applied. NEWFM includes the bounded sum of weighted fuzzy memberships (BSWFMs) that can easily show the differences in the graphically distinct characteristics between healthy people and PD patients. This study proposes new technique that NEWFM can detect PD patients from foot pressure data by the BSWFMs embedded in devices or systems.

中文翻译：

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使用 TAKAGI-SUGENO 模糊模型的实例选择和小波变换对健康人和 PD 患者进行分类

在这项研究中，提出了一种将神经网络与加权模糊隶属度（NEWFM）和Takagi-Sugeno（T-S）模糊模型相结合的新实例选择方法，以提高健康人和帕金森病（PD）患者的分类准确性。为了评估所提出的实例选择对健康人和 PD 患者的分类准确性，从健康人和 PD 患者中收集足部压力数据作为实验数据。本研究在预处理步骤中使用小波变换 (WT) 从足部压力数据中去除噪声。所提出的实例选择方法是一种算法，它使用 T-S 模糊模型中的加权平均去模糊化 (WMD) 和统计中使用的正态分布的置信区间来选择实例。对比实例选择前后的分类准确率，证明实例选择的优越性。实例选择前后的分类准确率分别为 77.33% 和 78.19%。实例选择后的分类准确率比实例选择前的分类准确率提高了0.86%。此外，统计中使用的 McNemar 检验用于显示应用实例选择之前和之后分类准确度的差异。McNemar 检验的结果显示显着性概率小于 0.05，这再次证实了应用实例选择时的分类准确度比不应用实例选择时更好。NEWFM 包括加权模糊隶属度 (BSWFM) 的有界总和，可以轻松显示健康人和 PD 患者之间图形上不同特征的差异。这项研究提出了新技术，NEWFM 可以通过嵌入设备或系统中的 BSWFM 从足压数据中检测 PD 患者。