Boosting-based decision tree for improved screening of vibroarthrographic signals

Abstract

Many diseases affect the knee joint, such as Chondromalica Pattelle (CP), which is the most bearing joint in the body. X-ray, MRI and arthroscopy are currently used for screening knee joint diseases. However, some of these techniques may be costly, dangerous as well as some of them being poor in functional resolution. On the other hand, researchers have shown the existence of variation in Vibroarthrography signal, recorded from the knee joint surface, between the normal and abnormal knee. VAG is the recording of vibrations generated from the knee joint surface, during flexion and extension, which may offer a tool of non-invasive screening for knee joint diseases. The main aim of this paper is to improve the VAG signal classification to diagnose CP. Simple time-domain features were used for the first time alongside boosting-based Decision Tree classifier. The area under the receiver operating characteristic curves was 0.816 which shows the effectiveness of the proposed features and boosting-based classifier compared to other methods.