{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T18:05:26Z","timestamp":1784657126298,"version":"3.55.0"},"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>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\u20138) and very strong (NRS 9\u201310) categories among patients with lumbar disc disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Cross-sectional study of 61 LBP patients (Numerical Rating Scale\u2009\u2265\u20097) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>RF achieved accuracy\u2009=\u20090.579 (95% CI: 0.334\u20130.800), AUC\u2009=\u20090.607 (0.340\u20130.875), specificity\u2009=\u20090.917, and sensitivity\u2009=\u20090.000. The strongest predictors were number of affected disc levels (MDG\u2009=\u20090.62), L4\u2013L5 disc location (MDA\u2009=\u20090.48), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec005\">\n                    <jats:title>Implications<\/jats:title>\n                    <jats:p>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\u2009\u2265\u2009200) and address class imbalance prior to considering clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec006\">\n                    <jats:title>Perspective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0354370","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T17:37:33Z","timestamp":1784655453000},"page":"e0354370","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of factors contributing to Pain Intensity among low back pain 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