Enhanced Prediction of Spine Surgery Outcomes Using Advanced Machine Learning Techniques and Oversampling Methods

Overview

Journal Health Inf Sci Syst

Publisher Springer

Specialty Medical Informatics

Date 2025 Mar 6

PMID 40046241

Authors

Jose Alberto Benitez-Andrades

Camino Prada-Garcia

Nicolas Ordas-Reyes

Marta Esteban Blanco

Alicia Merayo

Antonio Serrano-Garcia

Affiliations

Soon will be listed here.

Abstract

Purpose: Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance.

Methods: Various machine learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN with RandomOverSampler, KNN with SMOTE, and grid-searched optimized versions of KNN and Decision Tree, were applied to a dataset of 244 spine surgery patients. The dataset, comprising pre-surgical, psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study explored the impact of different variable groupings and oversampling techniques.

Results: Experimental results indicate that the KNN model, especially when enhanced with RandomOverSampler and SMOTE, demonstrated superior performance, achieving accuracy values as high as 76% and an F1-score of 67%. Grid-searched optimized versions of KNN and Decision Tree also yielded significant improvements in predictive accuracy and F1-score.

Conclusions: The study highlights the potential of advanced machine learning techniques and oversampling methods in predicting spine surgery outcomes. The results underscore the importance of careful variable selection and model optimization to achieve optimal performance. This system holds promise as a tool to assist healthcare professionals in decision-making, thereby enhancing spine surgery outcomes. Future research should focus on further refining these models and exploring their application across larger datasets and diverse clinical settings.

References

Khera R, Haimovich J, Hurley N, McNamara R, Spertus J, Desai N . Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiol. 2021; 6(6):633-641. PMC: 7948114. DOI: 10.1001/jamacardio.2021.0122. View

Finkelstein J, Stark R, Lee J, Schwartz C . Patient factors that matter in predicting spine surgery outcomes: a machine learning approach. J Neurosurg Spine. 2021; 35(1):127-136. DOI: 10.3171/2020.10.SPINE201354. View

Lawlor M, Rubery P, Thirukumaran C, Ramirez G, Fear K . Socioeconomic Status Correlates With Initial Patient-Reported Outcomes Measurement Information System-Pain Interference (PROMIS-PI) Scores but Not the Likelihood of Spine Surgery. Cureus. 2024; 16(3):e57281. PMC: 11057964. DOI: 10.7759/cureus.57281. View

Yang C . Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost. Math Biosci Eng. 2023; 20(6):10757-10772. DOI: 10.3934/mbe.2023477. View

Holbert S, Andersen K, Stone D, Pipkin K, Turcotte J, Patton C . Social Determinants of Health Influence Early Outcomes Following Lumbar Spine Surgery. Ochsner J. 2022; 22(4):299-306. PMC: 9753941. DOI: 10.31486/toj.22.0066. View

Greenberg J, Landman J, Kelly M, Pennicooke B, Molina C, Foraker R . Leveraging Artificial Intelligence and Synthetic Data Derivatives for Spine Surgery Research. Global Spine J. 2022; 13(8):2409-2421. PMC: 10538345. DOI: 10.1177/21925682221085535. View

Dietz N, Sharma M, Alhourani A, Ugiliweneza B, Wang D, Nuno M . Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions: a systematic review. Neurosurg Focus. 2018; 45(5):E10. DOI: 10.3171/2018.8.FOCUS18331. View

Pedersen C, Andersen M, Carreon L, Eiskjaer S . Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data. Global Spine J. 2020; 12(5):866-876. PMC: 9344505. DOI: 10.1177/2192568220967643. View

Lubelski D, Hersh A, Azad T, Ehresman J, Pennington Z, Lehner K . Prediction Models in Degenerative Spine Surgery: A Systematic Review. Global Spine J. 2021; 11(1_suppl):79S-88S. PMC: 8076813. DOI: 10.1177/2192568220959037. View

10.

Buda M, Maki A, Mazurowski M . A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018; 106:249-259. DOI: 10.1016/j.neunet.2018.07.011. View

11.

Zhou C, Hu J, Wang Y, Ji M, Tong J, Yang J . A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation. Sci Rep. 2021; 11(1):1571. PMC: 7810757. DOI: 10.1038/s41598-021-81188-6. View

12.

Elfanagely O, Toyoda Y, Othman S, Mellia J, Basta M, Liu T . Machine Learning and Surgical Outcomes Prediction: A Systematic Review. J Surg Res. 2021; 264:346-361. DOI: 10.1016/j.jss.2021.02.045. View

13.

Xiong Y, Ye M, Wu C . Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble. Comput Math Methods Med. 2021; 2021:5556992. PMC: 8093037. DOI: 10.1155/2021/5556992. View

14.

Xiong C, Zhao R, Xu J, Liang H, Zhang C, Zhao Z . Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm. Comput Math Methods Med. 2022; 2022:2697841. PMC: 9427297. DOI: 10.1155/2022/2697841. View

15.

Blagec K, Kraiger J, Fruhwirt W, Samwald M . Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals. J Biomed Inform. 2022; 137:104274. DOI: 10.1016/j.jbi.2022.104274. View

16.

Hopkins B, Mazmudar A, Driscoll C, Svet M, Goergen J, Kelsten M . Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions. Clin Neurol Neurosurg. 2020; 192:105718. DOI: 10.1016/j.clineuro.2020.105718. View

17.

Wu W, Li Y, Feng A, Li L, Huang T, Xu A . Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res. 2021; 8(1):44. PMC: 8356424. DOI: 10.1186/s40779-021-00338-z. View

18.

Wolf S, Holm S, Ingwersen T, Bartling C, Bender G, Birke G . Pre-stroke socioeconomic status predicts upper limb motor recovery after inpatient neurorehabilitation. Ann Med. 2022; 54(1):1265-1276. PMC: 9090381. DOI: 10.1080/07853890.2022.2059557. View

19.

Shaikhina T, Khovanova N . Handling limited datasets with neural networks in medical applications: A small-data approach. Artif Intell Med. 2017; 75:51-63. DOI: 10.1016/j.artmed.2016.12.003. View

20.

Tragaris T, Benetos I, Vlamis J, Pneumaticos S . Machine Learning Applications in Spine Surgery. Cureus. 2023; 15(10):e48078. PMC: 10689893. DOI: 10.7759/cureus.48078. View