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Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis

Year: 2025

Type: article

Abstract: Background and objective. Low back pain (LBP) is considered the most common and challenging disorder in health care. Although its incidence increases with age, a student`s sedentary behavior could contribute to this risk. Through machine learning (ML), advanced algorithms can analyze complex patterns in health data, enabling accurate prediction and targeted prevention of medical conditions such as LBP. This study aims to detect the factors associated with LBP among health sciences students. Methods. A self-administered modified version of the Standardized Nordic Questionnaire was completed in May to June 2022 by freshman class health sciences students. A supervised random forest algorithm was utilized to analyze data and prioritize the importance of variables related to LBP. The model's predictive capability was further visualized using a decision tree to identify high-risk patterns and associations. Results. A total of 197/222 (88.7%) students participated in this study, most of whom (75%) were female. Their mean age and body mass index were 23 ±3.8 and 23 ±3.5, respectively. Forty-six percent (n=90) of the students reported having experienced LBP in the last month, 15% (n=30) were smokers, and 60% (n=119) were involved in prolonged sitting (more than 3 hours per day). The decision tree of ML revealed that a history of pain (score=1), as well as disability (score= 0.34) and physical activity (score =0.21), were significantly associated with LBP. Conclusions. About half (46%) of the health science students suffered from LBP. The machine-learning approach showed that a history of pain was the most independent factor related to LBP.

Source: Journal of Clinical Medicine

DOI: 10.3390/jcm14062046

Authors: Janan Abbas, Malik Yousef, Kamal Hamoud, Katherin Joubran

Institution: Zefat Academic College

Language: English

Topic: Musculoskeletal pain and rehabilitation

Subfield: Pharmacology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137