Improving the prediction accuracy of low back pain using machine learning through data pre-processing techniques
by G. Ganapathy; N. Sivakumaran; M. Punniyamoorthy; Tryambak Chatterjee; Monisha Ravi
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 13, No. 1, 2021

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.

Online publication date: Fri, 18-Dec-2020

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