Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost).
Authors
Affiliations (2)
Affiliations (2)
- Community and Psychiatric Health Nursing Department, Faculty of Nursing, University of Tabuk, Tabuk, Saudi Arabia.
- Department of Radiological Sciences, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Abstract
Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain intensity is shaped by multifaceted interactions among demographic, lifestyle, and clinical factors. This study aimed to predict factors contributing to pain intensity in adults with lower back pain (LBP) using Random Forest (RF) and XGBoost models. It evaluated the association between lifestyle factors and lumbar spine MRI abnormalities, classifying pain intensity into strong (NRS 7-8) and very strong (NRS 9-10) categories among patients with lumbar disc disorders. Cross-sectional study of 61 LBP patients (Numerical Rating Scale ≥ 7) at King Fahad Specialist Hospital, Saudi Arabia. Predictors included demographics (age, sex, body mass index), MRI findings (disc location, number of affected levels, pathology type), and lifestyle factors (exercise, sitting time). Random Forest (500 trees, 70/30 train-test split, 5-fold cross-validation) and XGBoost were compared. RF achieved accuracy = 0.579 (95% CI: 0.334-0.800), AUC = 0.607 (0.340-0.875), specificity = 0.917, and sensitivity = 0.000. The strongest predictors were number of affected disc levels (MDG = 0.62), L4-L5 disc location (MDA = 0.48), age (0.31), and exercise time (0.28). XGBoost achieved 66.67% accuracy but sensitivity of only 0.33, likely due to class imbalance (72.1% very strong pain). RF outperformed XGBoost in overall stability; XGBoost provided complementary feature-level insights via SHAP. These findings highlight the potential of machine learning as a decision-support tool for identifying pain-related risk factors in LBP. RF demonstrated limited predictive utility in its current form, insufficient for clinical application. Future research should involve multi-center designs with larger sample sizes (n ≥ 200) and address class imbalance prior to considering clinical translation. This study demonstrates how integrating lumbar MRI findings with machine learning improves pain intensity prediction in low back pain, supporting more objective risk stratification and informed clinical decision-making.