Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision

Jingye Yee, Jingye Yee and Cheng Yee Low, Cheng Yee Low and Mohamad Hashim, Natiara and Che Zakaria, Noor Ayuni and Johar, Khairunnisa and Othman, Nurul Atiqah and Hock Hung Chieng, Hock Hung Chieng and Hanapiah, Fazah Akhtar (2023) Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision. Diagnostic, 13 (739). pp. 1-31.

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: The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction.

Item Type: Article
Uncontrolled Keywords: spasticity; Modified Ashworth Scale; machine learning; medical expert system
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Mechanical and Manufacturing Engineering
Depositing User: Mr. Mohamad Zulkhibri Rahmad
Date Deposited: 30 Jul 2023 07:09
Last Modified: 30 Jul 2023 07:09

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