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A Comparative Analysis of Classification Methods for Diagnosis of Lower Back Pain


Affiliations
1 MCA Department, at ITM Universe, Affiliated to Gujarat Technological University, Vadodara, India
2 MCA and M.Sc.(IT), at Indus University, Ahmedabad, India
3 Space Application Centre, ISRO, Ahmedabad, India
 

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. 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 modeling, Kaggle. The experiments were carried out in WEKA (Waikato Environment for Knowledge Analysis), suite of machine learning software.

Keywords

Classification Methods, Lower Back Pain, NaiveBayes, Multilayer Perceptron, Base Level, Meta Level.
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  • A Comparative Analysis of Classification Methods for Diagnosis of Lower Back Pain

Abstract Views: 313  |  PDF Views: 4

Authors

Mittal Bhatt
MCA Department, at ITM Universe, Affiliated to Gujarat Technological University, Vadodara, India
Vishal Dahiya
MCA and M.Sc.(IT), at Indus University, Ahmedabad, India
Arvind K. Singh
Space Application Centre, ISRO, Ahmedabad, India

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. 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 modeling, Kaggle. The experiments were carried out in WEKA (Waikato Environment for Knowledge Analysis), suite of machine learning software.

Keywords


Classification Methods, Lower Back Pain, NaiveBayes, Multilayer Perceptron, Base Level, Meta Level.

References