Improving the prediction accuracy of low back pain using machine learning through data pre-processing techniques. (17th November 2020)

• Record Type: Journal Article
• Authors: Ganapathy, G.; Sivakumaran, N.; Punniyamoorthy, M.; Chatterjee, Tryambak; Ravi, Monisha
• Is Part Of: International journal of medical engineering and informatics. Volume 13:Number 1(2021)
• Page Start: 92
• Page End: 102
• Publication Date: 2020-11-17
• Subjects: K-nearest neighbour -- K-NN -- logistic regression -- low back pain -- support vector machine -- SVM -- prediction -- naïve Bayes; 610.2805
• Languages: English
• ISSNs: 1755-0653

Abstract

Application of machine learning algorithms in the healthcare industry has been increasing by many folds. Low back pain has caused problems to many persons all around the world. An early treatment or detection of whether a person has the symptoms pertaining to low back pain can help faster medication and treatment of the patient and help them with getting their medical condition degraded. This paper focuses on four different machine learning algorithms vis. SVM, logistic regression and naïve Bayes which can be used to predict whether a person is suffering from low back pain or not. Finally, the modification is carried out in naïve Bayes algorithm to enhance the performance of the algorithm. The Kaggle dataset is adopted to validate the machine-learning algorithm. The accuracy of each algorithm is compared.