Ensemble Learning and Stacking Classifier for Effective Detection of Lumbar Spondylolisthesis in Spinal Imaging

Dr. Deepika Saravagi

Dr. Deepika Saravagi Assistant Professor, Patkar Varde College, Goregaon, mumbai, Maharaashtra

Keywords: Lumbar Spondylolisthesis, Ensemble Learning, Stacking Classifier, Bagging, Random Forest, KNN, Machine Learning

Abstract

Lumbar spondylolisthesis, defined by the displacement of one vertebra over another, requires accurate diagnosis to inform treatment approaches and prevent any consequences. Ensemble learning methods in machine learning models have shown promise in improving classification accuracy. This study analyzes the utilization of diverse ensemble learning models, including Bagging, Random Forest, K-Nearest Neighbors (KNN), and Stacking Classifiers, for the identification of lumbar spondylolisthesis. The Stacking Classifier had exceptional performance, attaining an accuracy of 97%, above that of both individual models and homogeneous ensemble techniques. The results demonstrate that ensemble learning, especially stacking, is an effective technique for identifying lumbar spondylolisthesis, offering a reliable tool for clinical decision-making.

References

Altaf, F., Gibson, A., & Dannawi, Z. (2007). The clinical and radiological outcomes of lumbar spondylolisthesis. Journal of Bone and Joint Surgery, 89(5), 636-642.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794).

Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15). Springer, Berlin, Heidelberg.

Gangeh, M. J., Law, M., & Cheng, S. (2018). Ensemble methods in spine imaging research. European Spine Journal, 27(6), 1327-1340.

Lao, J., Chen, Y., Li, H., & Liu, W. (2017). Automated detection of spinal conditions using machine learning methods. Medical Imaging Journal, 32(4), 241-256.

Lu, Y., Lu, J., & Wang, C. (2018). Automated lumbar spine analysis in clinical settings. Journal of Digital Imaging, 31(6), 831-841.

Matsunaga, S., & Sakou, T. (2000). Spondylolisthesis: Etiology and natural history. Journal of Orthopaedic Science, 5(3), 1-5.

Patel, V., Shah, S., & Mehta, P. (2015). Radiographic analysis of spinal disorders: A clinical perspective. Clinical Radiology, 60(1), 49-55.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106.

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48.

Sill, J., Teschke, M., Grotthoff, S., & Schmidt, S. (2009). Advantages of ensemble methods in complex datasets. Neural Processing Letters, 25(1), 57-68.

Sun, J., Wang, X., & Zhang, L. (2018). Stacking models in medical imaging for high-precision classification. IEEE Transactions on Medical Imaging, 37(12), 2499-2508.

Ting, K. M., & Witten, I. H. (1999). Issues in stacked generalization. Journal of Artificial Intelligence Research, 10, 271-289.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Vapnik, V. (1998). Statistical learning theory. Wiley-Interscience.

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.

Zhou, Y., Shi, L., Zhang, Y., & Hu, Y. (2019). Machine learning applications in spinal imaging. Journal of Spine Surgery, 5(1), 21-30.