Classification of patients and controls based on stabilogram signal data

Abstract

Inner ear balance problems are common worldwide and are often difficult to diagnose. In this study we examine the classification of patients with inner ear balance problems and controls (people not suffering from inner ear balance problems) based on data derived from the stabilogram signals and using machine learning algorithms. This paper is a continuation for our earlier paper where the same dataset was used and the focus was medically oriented. Our collected dataset consists of stabilogram (a force platform response) data from 30 patients suffering from Ménière’s disease and 30 students called controls. We select a wide variety of machine learning algorithms from traditional baseline methods to state-of-the-art methods such as Least-Squares Support Vector Machines and Random Forests. We perform extensive and carefully made parameter value searches and we are able to achieve 88.3% accuracy using $k$ -nearest neighbor classifier. Our results show that machine learning algorithms are well capable of separating patients and controls from each other.