scholarly journals Detecting Lower Back Pain Using Stacked Ensemble Approach

Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta

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.

2000 ◽  
Vol 53 (3) ◽  
pp. 595-624 ◽  
Author(s):  
JOSEPH J. MARTOCCHIO ◽  
DAVID A. HARRISON ◽  
HOWARD BERKSON

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Li-sheng Hou ◽  
Dong Zhang ◽  
Feng Ge ◽  
Hai-feng Li ◽  
Tian-jun Gao

Abstract Background Previous research and published literature indicate that some patients with spinal diseases who underwent percutaneous transforaminal endoscopic decompression (PTED) still suffer some discomfort in the early recovery stage in the form of pain, stiffness, and swelling. These are usually considered minor residual symptoms or normal postoperative phenomenon (NPF) in the clinic, occur frequently, and are acknowledged by surgeons worldwide. To the best of our knowledge, we report the first case of a patient who had an osteoporotic vertebral fracture (OVF) misdiagnosed as NPF after she underwent PTED as a result of lumbar disc herniation (LDH). Case presentation A 71-year-old female with Parkinson’s disease who presented with lower back pain radiating to the legs was diagnosed as LDH in L4–5, after which a PTED of L4–5 was performed, with temporary alleviation of symptoms. However, severe lower back pain recurred. Unfortunately, the recurred pain initially misdiagnosed as NPF, in fact, was finally confirmed to be OVF by CT-scan. OVF in the early stage of post-PTED seldom occurs and is rarely reported in the literature. With a percutaneous vertebroplasty, the pain was significantly relieved, and she resumed walking. After 36-weeks of follow-up, the pain improved satisfactorily. Conclusion Doctors should not immediately diagnose a relapse of back pain following PTED as NPF, and hands-on careful physical and imaging examinations are necessary to manage recurring pain rightly and timely.


Author(s):  
Katsiaryna Prudnikova ◽  
David Jamison ◽  
Michele Marcolongo

Intervertebral disc degeneration and associated lower back pain is one of the leading musculoskeletal disorders confronting our health system with 15%–20% of the population experiencing lower back pain annually [1–4]. It has been shown that early in disc degeneration, the extracellular matrix of the nucleus pulposus is depleted of the proteoglycan aggrecan, resulting in loss of disc hydration, osmotic pressure and mechanical stability which leads to lower back pain [2, 5]. Early-stage restoration of the proteoglycan content within normal levels with natural aggrecan may help to restore disc functionality but it is cost prohibitive. We propose a new strategy to restore the extracellular matrix of the degenerated disc and mitigate lower back pain by molecularly engineering the disc matrix with an injection of a biomimetic aggrecan (BA) novel class of molecules that mimics the 3D bottle brush structure and physical properties of natural aggrecan.


2010 ◽  
Vol 38 (9) ◽  
pp. 24
Author(s):  
ELIZABETH MECHCATIE
Keyword(s):  

Author(s):  
Ibrahim Alburaidi ◽  
Khaled Alravie ◽  
Saleh Qahtani ◽  
Hani Dibssan ◽  
Nawaf Abdulhadi ◽  
...  

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