ArticleJournal of back and musculoskeletal rehabilitation2024
Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.
Article in Journal of back and musculoskeletal rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI-assisted haematology: machine learning-based prediction of iron-deficiency anaemia from reticulocyte maturation indices.BMC medical informatics and decision making · 2025Article
- InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients.Sensors (Basel, Switzerland) · 2025Article
- Oxygen-ozone therapy for musculoskeletal pain in rehabilitation: Evidence and future perspectives.Journal of back and musculoskeletal rehabilitation · 2024Article
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Abstract
backgroundLow back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP.
objectiveThe purpose of this study was to develop and compare efficient low back pain prediction models using easily obtainable demographic and lifestyle factors.
methodsData from adult men and women aged 50 years or older collected from the Korean National Health and Nutrition Examination Survey (KNHANES) were used. The dataset included 22 predictor variables, including demographic, physical activity, occupational, and lifestyle factors. Four machine learning algorithms, including XGBoost, LGBM, CatBoost, and RandomForest, were used to develop predictive models.
resultsAll models achieved an accuracy greater than 0.8, with the LGBM model outperforming the others with an accuracy of 0.830. The CatBoost model had the highest sensitivity (0.804), while the LGBM model showed the highest specificity (0.884) and F1-Score (0.821). Feature importance analysis revealed that EQ-5D was the most critical variable across all models.
conclusionIn this study, an efficient LBP prediction model was developed using easily accessible variables. Using this model, it may be helpful to identify the risk of LBP in advance or establish prevention strategies in subjects who have difficulty accessing medical facilities.
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