Application of Machine Learning Techniques for the Classification of Lower Back Pain in Human Body

Abstract

Advancement of technology in the field of medical science is providing promising results in this modern era. Intelligent systems designed especially for this sector not only help doctors to solve complex situations but also take comparatively lesser computational time which can be a really important factor as longer time in critical cases may lead to death of a patient. These days, everyone experiences long waiting time to see a doctor. It would be really desirable if an Intelligent System makes the work easy for a doctor by giving accurate decisions after processing patient’s data, reducing examination time for the current patient and hence reducing waiting sessions for other patients. Currently, “Lower back pain is one of the biggest problems being faced by more than 80% of the population at least once during their lifetime” [1]. Its diagnosis at early stages is necessary in order to find a proper cure. Along with Conventional Medical Diagnostic Systems, Various Non-Conventional techniques are used for the successful classification of Lower Back Pain symptoms categorised as normal and abnormal. Naïve Bayes, Artificial Neural Networks, Logistic Regression, Deep Learning, Fast Large Margin, Random Forest, Gradient Boosted Trees, Multi-Layer Perceptron, K-Nearest Neighbour, Decision Tree and Support Vector Machine methods are most suitable machine learning techniques which can classify given dataset with good accuracy. The aim of this research is the application of several machine learning techniques to correctly classify Spine Dataset and finding best technique among those in terms of Accuracy, Precision, Sensitivity, Specificity, and F-measure [2]. Original dataset is taken from website named Kaggle (https://www.kaggle.com/). This dataset is normalized first and then an Automatic Feature Engineering technique has been implemented on the dataset to extract the most important features to do the correct classification. Training of each model is performed using II featured data and after training, each algorithm is tested and hence performance is calculated and compared. After analysing results, it is found that for the problem considered the Logistic Regression algorithm is the best classifier in terms of Accuracy giving 90.91% accurate results on test data followed by an Artificial Neural Network algorithm whose accuracy is 88.64%. In terms of Precision calculation, the Logistic Regression is best and the ANN Classifier is second best algorithm. Taking Sensitivity into Consideration, the Fast-Large Margin is best. ANN Classifier is best in terms of Specificity. Logistic Regression provides best results in terms of AUC (Area under Curve).