Differentiation between Peripheral and Non-Peripheral Vestibular Diseases by Machine Learning of Stabilometry

Using machine learning, we attempted to differentiate between peripheral vestibular disorders (n = 466) and non-peripheral vestibular disorders (n = 254) based on the results of stabilometry. Six algorithms were used for machine learning: random forest, gradient boosting, support vector machine, logistic regression, k-nearest neighbor, and multilayer perceptron. Due to the large difference in the amount of data between the two groups, SMOTE (Synthetic Minority Over-sampling Technique) was used during learning to correct for the amount of data between the two groups.