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A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis

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Abstract

In this paper, an interpretable classifier for Parkinson’s Disease (PD) diagnosis based on analyzing the gait cycle is presented. The proposed method utilizes clinical features extracted from the vertical Ground Reaction Force (vGRF) measured by wearable sensors. Therefore, experts can verify the decision made by the proposed method. Type-2 fuzzy logic is applied to increase the robustness against noisy sensor data. First, the initial fuzzy rules are extracted using a K-Nearest-Neighbor-based clustering approach. Next, a novel quasi-Levenberg-Marquardt (qLM) learning approach is proposed and applied to fine-tune the initial rules based on minimizing the cross-entropy loss function using a trust-region optimization method. Finally, complementary online learning is proposed to improve rules by encountering new labeled samples. The performance of the method is evaluated to classify patients and healthy subjects in different conditions, including the presence of noise or observing new samples. Moreover, the performance of the model is compared to some previous supervised and unsupervised machine learning approaches. The final Accuracy, Precision, Recall, and F1 Score of the proposed method are 97.61%, 97.58%, 99.02%, and 98.30%.

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Notes

  1. https://physionet.org/content/gaitpdb/1.0.0/

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  1. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

    Armin Salimi-Badr, Mohammad Hashemi & Hamidreza Saffari

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Salimi-Badr, A., Hashemi, M. & Saffari, H. A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis. Appl Intell 53, 15656–15682 (2023). https://doi.org/10.1007/s10489-022-04276-8

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