ArticlePloS one2026
Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost).
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting 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.
aimThis 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.
methodsCross-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.
resultsRF 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. IMPLICATIONS: 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. PERSPECTIVE: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.