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


Different classification approaches are compared in this research for the accurate diagnosis of Lower Back Pain using base and meta (Combination of Multiple Classifier for Training) level classifiers. Different imaging modalities based on radiology are also evaluated for diagnosing Lower Back Pain, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) (MRI). Lower back pain gets persistent as people age, so it’s important to get a proper diagnosis early on. At the base and meta levels, five separate classifiers were implemented. Five distinct classifier combinations were developed at the meta level, using a voting mechanism. The overall classification utilising Nave Bayes and Multilayer Perceptron had the highest efficiency of 83.87 percent, according to the results. The goal of this study is to efficiently diagnose healthy people. to investigate the symptoms of lower back pain The dataset comes from Kaggle, a well-known predictive modelling site. The studies were conducted using the WEKA (Waikato Environment for Knowledge Analysis) software suite [1].

Author (S) Details

Dr. Mittal Desai
MCA Department, CMPICA, CHARUSAT University, Changa, India.

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