A Convenient Non-harm Cervical Spondylosis Intelligent Identity method based on Machine Learning

Abstract

Cervical spondylosis(CS), a most common orthopedic diseases, is mainly identified by the doctors judgment from the clinical symptoms and cervical change provided by expensive instruments in hospital. Owing to the development of the surface electromyography(sEMG) technique and artificial intelligence, we proposed a convenient non-harm CS intelligent identify method EasiCNCSII, including the sEMG data acquisition and the CS identification. For the convenience and efficiency of data acquisition with the limited testable muscles provided by the sEMG technology, we proposed a data acquisition method based on the relationship between muscle activity pattern, the tendons theory and CS etiology. It is easily performed in less than 20 minutes, even outside the hospital. Faced with the challenge of high-dimension and the weak availability, the 3-tier model EasiAI is developed to intelligently identify CS. The common features and new features are extracted from raw sEMG data in first tier. The EasiRF is proposed in second tier to further reduce the data dimension and improve the performance. With the limited and weakly available data, the gradient boosted regression tree is developed in third tier to effectively identify CS. The EasiAI achieve the best performance with 91.02% in accuracy, 97.14% in sensitivity, and 81.43% in specificity compared with 4 common machine learning classification model, validating the EasiCNCSII effectiveness.