A Comparative Study of Diagnosis of Lower Back Pain Based on Classification and Imaging Techniques

Abstract

In this paper different classification methods are compared using base and meta (Combination of Multiple Classifier for training) level classifiers, for the fruitful diagnosis of Lower Back Pain. Radiology based different imaging techniques are also compared for diagnosing Lower Back Pain, like Computed Tomography (CT)  scan, Magnetic Resonance Imaging (MRI).  The Lower Back Pain becomes chronic with age, so needs to be correctly diagnose with symptoms in the early age. Five independent classifiers were implemented at base level and meta level. At meta level, five combinations of different classifiers were implemented, using voting technique. According to the scores, the overall classification using Naïve Bayes and Multilayer Perceptron got the maximum efficiency 83.87%. The purpose of this paper is to diagnose healthy individuals efficiently. To carry out study the Lower Back Pain Symptoms Dataset is used from very famous platform for predictive modelling, Kaggle. The experiments were carried out in WEKA (Waikato Environment for Knowledge Analysis), suite of machine learning software [1].