DOI: 10.26650/acin.1704458 ISSN: 2602-3563

Predicting Low Back Pain Using Machine Learning Techniques: A Systematic Review

Demet Köseoğlu, Mehmet Akkoyunlu
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Low back pain (LBP) is one of the leading causes of disability worldwide, posing substantial clinical and socioeconomic challenges. Machine learning (ML) has introduced new opportunities for improving early diagnosis, risk stratification, and treatment guidance in LBP management. This systematic review aims to synthesize current evidence on the application of ML techniques in the diagnosis and clinical management of LBP and to identify methodological patterns and performance trends.Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 27 peer-reviewed studies published between 2017 and 2023 were systematically identified and evaluated according to predefined inclusion and exclusion criteria.The findings indicate that Convolutional Neural Networks (CNNs) achieve high accuracy rates (98%–99%) in large-scale and imaging-based datasets, such as magnetic resonance imaging (MRI) and EHR data. SVMs demonstrate strong predictive performance in real-time sensor signal analysis, while ensemble methods, including Random Forest and XGBoost, yield robust results in structured clinical and biomarker datasets. Nevertheless, substantial methodological barriers remain. These include data heterogeneity and noise in PROM–based datasets, performance decline during external validation in real-world clinical settings, and limited interpretability due to the black-box nature of many ML models.This review highlights the need for multimodal data fusion strategies and the integration of Explainable Artificial Intelligence (XAI) frameworks to enhance model transparency, generalizability, and clinical trust. Advancing these dimensions is critical for translating ML–based LBP models from experimental settings into routine clinical decision support systems and for shifting healthcare delivery from reactive management to proactive, data-driven care ecosystems.

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