Integrating Clinical and Radiological Features for Lumbosacral Radiculopathy (Sciatica) Prediction and a Comparative Analysis of Various Machine Learning Approaches

Abstract

Sciatica is a neurological condition characterized by Compression of the sciatic nerve causes pain that radiates from the legs to the lower back. Conventional diagnostic approaches, including physical examinations and MRI analysis, are time-consuming, prone to human error, and limited by subjective interpretation. AI and ML have revolutionized industries with their emergence medical diagnostics, offering data-driven solutions to improve accuracy and reduce diagnostic uncertainty. The study assesses various ML models—such as Decision Trees, SVM, Random Forests, Neural Networks, and Gradient Boosting—in predicting sciatica using clinical and imaging data. Recent research suggests that ensemble methods like Random Forest and Gradient Boosting often outperform conventional models in predictive performance, making them strong candidates for sciatica diagnosis. However, the interpretability of complex models, such as deep learning architectures, remains a crucial factor in clinical adoption. The study further evaluates the trade-offs between predictive accuracy and model explainability to determine the most suitable ML approach for real-world clinical applications. Additionally, AI-driven diagnostic systems can facilitate early detection, reduce the risk of chronic pain, and minimize the need for invasive procedures. To the research, this contributes findings the advancement of intelligent diagnostic tools in musculoskeletal healthcare, enhancing clinical decision-making, optimizing diagnostic workflows, and improving patient outcomes. Study highlights the potential of AI in revolutionizing sciatica diagnosis and provides insights into selecting an optimal ML model for effective implementation in clinical practice. In addition, the study incorporates insights from recent deep learning research. Furthermore, AI-driven diagnostic systems offer the potential for early detection, reduced risk of chronic pain progression, and minimized reliance on invasive procedures. Integrating these predictive tools into telemedicine platforms could also enhance access to specialized care in underserved regions. The study underscores the transformative role of AI, particularly machine learning, in the future of sciatica diagnosis.