AI Article Synopsis
- Parkinson's disease (PD) is a progressive neurological disorder that primarily affects movement, and objective assessments can enhance the care provided to patients.
- This study aimed to create data-driven models using regression algorithms to analyze kinematic features from motor tasks performed by 64 individuals with PD and 50 healthy controls, using wearable sensors for data collection.
- The findings highlighted that the adaptive neuro-fuzzy inference system (ANFIS) achieved the highest prediction accuracy (correlation coefficient of 0.814), suggesting its potential as a helpful tool for clinicians in objectively assessing the severity of PD based on patients' motor performance.
Article Abstract
Parkinson's disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients' quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723941 | PMC |
| http://dx.doi.org/10.1007/s10439-020-02628-4 | DOI Listing |
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