Shubham Sharma and Rene V.Mayorga, University of Regina, Canada

In today’s world, the problem of lower back pain is one of the fastest growing crucial ailments to deal with. More than half of total population on the earth, suffers from it at least once in a lifetime. Human Lower Back Pain symptoms are commonly categorized as Normal or Abnormal. In order to remedy Human Lower Back Pain, with the growth of technology over the time, many medical methods have been developed to diagnose and cure this pain at its earliest stage possible. This study aims to develop two Machine Learning (M.L.) models which can classify Human Lower Back Pain symptoms in a human body using non-conventional techniques such as Feedforward/Backpropagation Artificial Neural Networks, and Fully Connected Deep Networks. An Automatic Feature Engineering technique is implemented to extract featured data used for the classification. The proposed models are compared with respect to a Support Vector Machine model; considering different performance parameters.

Machine Learning, Artificial Neural Networks, Fully Connected Deep Networks, Support Vector Machine, Lower Back Pain, Automatic Feature Engineering technique