Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Objective: To evaluate various machine learning algorithms in predicting peripheral vestibular dysfunction using the dataset of the center of pressure (COP) sway during foam posturography measured from patients with dizziness. Study Design: Retrospective study. Setting: Tertiary referral center. Patients: Seventy-five patients with vestibular dysfunction and 163 healthy controls were retrospectively recruited. The dataset included the velocity, the envelopment area, the power spectrum of the COP for three frequency ranges and the presence of peripheral vestibular dysfunction evaluated by caloric testing in 75 patients with vestibular dysfunction and 163 healthy controls. Main Outcome Measures: Various forms of machine learning algorithms including the Gradient Boosting Decision Tree, Bagging Classifier, and Logistic Regression were trained. Validation and comparison were performed using the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the recall of each algorithm using K-fold cross-validation. Results: The AUC (0.90 ± 0.06) and the recall (0.84 ± 0.07) of the Gradient Boosting Decision Tree were the highest among the algorithms tested, and both of them were significantly higher than those of the logistic regression (AUC: 0.85 ± 0.08, recall: 0.78 ± 0.07). The recall of the Bagging Classifier (0.82 ± 0.07) was also significantly higher than that of logistic regression. Conclusion: Machine learning algorithms can be successfully used to predict vestibular dysfunction as identified using caloric testing with the dataset of the COP sway during posturography. The multiple algorithms should be evaluated in each clinical dataset since specific algorithm does not always fit to any dataset. Optimization of the hyperparameters in each algorithm are necessary to obtain the highest accuracy.