Title: Improving the prediction accuracy of low back pain using machine learning through data pre-processing techniques

Authors: G. Ganapathy; N. Sivakumaran; M. Punniyamoorthy; Tryambak Chatterjee; Monisha Ravi

Addresses: Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, 620 015, Tamilnadu, India ' Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli, 620 015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli, 620 015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli, 620 015, India

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.

Keywords: K-nearest neighbour; K-NN; logistic regression; low back pain; support vector machine; SVM; prediction; naïve Bayes.

DOI: 10.1504/IJMEI.2021.111868

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.1, pp.92 - 102

Received: 16 Aug 2018
Accepted: 02 Feb 2019
Published online: 18 Dec 2020 *

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