Key result
Machine learning algorithms using Centre of Pressure time series diagnosed balance disorders with an f1-score of 76.47% for logistic regression and 76.19% for support vector machines.
Why the study?
Standard Sensory Organization Test calculation for balance disorders uses only two measurements reflecting maximum sway angles, leaving an opportunity to analyze the full Centre of Pressure time series.
Can machine learning algorithms using Centre of Pressure time series data accurately diagnose balance disorder impairments in patients?
Observational (n=475)
Can machine learning algorithms using Centre of Pressure time series data accurately diagnose balance disorder impairments in patients?
Absolute Event Rate: 76.47% vs 76.19%
Machine learning algorithms, particularly logistic regression, can utilize Centre of Pressure time series data to diagnose balance disorders with moderate accuracy.
May aid research on ML-based balance assessment from COP data; leaves open prospective validation before any clinical use.
Balance disorders are caused by several factors related to functionality deficits in one or multiple sensory systems such as vision, vestibular, and somatosensory systems. Patients usually have difficulty explaining their dizziness, often using ambiguous words to describe their symptoms. A common practice by clinicians is to objectively evaluate the patient's dizziness by applying the Sensory Organization Test (SOT), which measures the contribution of each sensory system (vestibular, visual, somatosensory). The SOT protocol can record up to 2000 measurements in 20 s to generate the Equilibrium Score (EQS) with its five load sensors. EQS is an indicator that reflects how well a patient can maintain balance. However, its calculation only considers two instances from these 2000 measurements that reflect the maximum anterior and posterior sway angle during the test performance; therefore, there is an opportunity to perform further analysis. This article aims to use the Centre of Pressure (COP) time series generated by the SOT and describes a methodology to pre-process and reduce the dimensionality of this raw data and use it as an input for machine learning algorithms to diagnose patients with balance disorder impairments. After applying this methodology to data from 475 patients, the logistic regression model (LR) produced the highest f1-score with 76.47%, and the support vector machine (SVM) performed almost as well, with an f1-score of 76.19%.
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Rojas et al. (2022) conducted an observational in Balance disorders (n=475). Machine learning algorithms (Logistic Regression and SVM) was evaluated on f1-score for diagnosing balance disorder impairments. Machine learning algorithms using Centre of Pressure time series diagnosed balance disorders with an f1-score of 76.47% for logistic regression and 76.19% for support vector machines.
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