Classification of locomotive disorders based on stance parameters

The use of technology has grown extensively in many fields, including that of the medical sciences. However, there has not been a lot of research done in the field regarding the use of basic stance parameters to classify the presence or lack thereof of locomotive disorders. In this paper, we shall be presenting the most optimum classification algorithm for the binary classification of a variety of locomotive disorders based on the stance parameters of the subject. The paper initially discusses the various advancements that machine learning algorithms have made in classifying a variety of medical disorders in order to illustrate the presence and scope of these techniques. Comparison analysis has also been performed based on various machine learning algorithms to classify the data. At the end, the paper presents the most optimum machine learning algorithm based on the accuracy parameters such as true positive, true negative, false positive and false negative.