Version 1
: Received: 13 July 2020 / Approved: 14 July 2020 / Online: 14 July 2020 (11:31:46 CEST)
How to cite:
Bandyopadhyay, S.; Dutta, S. Detecting Lower Back Pain Using Stacked Ensemble Approach. Preprints2020, 2020070303. https://doi.org/10.20944/preprints202007.0303.v1.
Bandyopadhyay, S.; Dutta, S. Detecting Lower Back Pain Using Stacked Ensemble Approach. Preprints 2020, 2020070303. https://doi.org/10.20944/preprints202007.0303.v1.
Cite as:
Bandyopadhyay, S.; Dutta, S. Detecting Lower Back Pain Using Stacked Ensemble Approach. Preprints2020, 2020070303. https://doi.org/10.20944/preprints202007.0303.v1.
Bandyopadhyay, S.; Dutta, S. Detecting Lower Back Pain Using Stacked Ensemble Approach. Preprints 2020, 2020070303. https://doi.org/10.20944/preprints202007.0303.v1.
Abstract
Lower Back Pain (LBP) is a disease that needs immediate attention. Person with back pain shall go immediately to doctor for treatment. Injury, excessive works and some medical conditions are result of back pain. Back pain is common to any age of human for different reasons. Due to factors such as previous occupation and degenerative disk disease the chance of developing lower back pain increases for older people. It hampers the working condition of people common reason for seeking medical treatment. The result is absence from work and is unable to normal due to pain. It creates uncomfortable and debilitating situations. Hence, detecting this disease at an early stage will assist the medical field experts to suggest counter measures to the patients. Detection of lower back pain is implemented in this paper by applying ensemble machine learning technique. This paper proposes Stacking ensemble classifier as an automated tool that will predict lower back pain tendency of a patient. Experimental result implies that the proposed method reaches an accuracy of 76.34%, f1-score of 0.76 and MSE of 0.34.
Keywords
Lower back pain; automated tool; ML; ensemble technique; stacked generalization
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
