出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
关键词：Machine Learning; Artificial Neural Networks; Fully Connected Deep Networks; Support Vector
Machine; Lower Back Pain; Automatic Feature Engineering technique;
