{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:15:05Z","timestamp":1781615705707,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100022664","name":"Universidad de Le\u00f3n","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100022664","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s13755-025-00343-9","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:10:16Z","timestamp":1741097416000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4450-349X","authenticated-orcid":false,"given":"Jos\u00e9 Alberto","family":"Ben\u00edtez-Andrades","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camino","family":"Prada-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00e1s","family":"Ord\u00e1s-Reyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta Esteban","family":"Blanco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alicia","family":"Merayo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Serrano-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"343_CR1","doi-asserted-by":"publisher","first-page":"79S","DOI":"10.1177\/2192568220959037","volume":"11","author":"D Lubelski","year":"2021","unstructured":"Lubelski D, Hersh A, Azad T, Ehresman J, Pennington Z, Lehner K, et al. Prediction models in degenerative spine surgery: a systematic review. Global Spine J. 2021;11:79S-88S. https:\/\/doi.org\/10.1177\/2192568220959037.","journal-title":"Global Spine J"},{"key":"343_CR2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.57281","author":"M Lawlor","year":"2024","unstructured":"Lawlor M, Rubery P, Thirukumaran C. 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. https:\/\/doi.org\/10.7759\/cureus.57281.","journal-title":"Cureus"},{"key":"343_CR3","doi-asserted-by":"publisher","first-page":"299","DOI":"10.31486\/toj.22.0066","volume":"22","author":"S Holbert","year":"2022","unstructured":"Holbert S, Andersen K, Stone D. Social determinants of health influence early outcomes following lumbar spine surgery. Ochsner J. 2022;22:299.","journal-title":"Ochsner J"},{"issue":"1","key":"343_CR4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3171\/2020.10.SPINE201354","volume":"35","author":"J Finkelstein","year":"2021","unstructured":"Finkelstein J, Stark RB, Lee J, Schwartz C. Patient factors that matter in predicting spine surgery outcomes: a machine learning approach. J Neurosurg Spine. 2021;35(1):127\u201336. https:\/\/doi.org\/10.3171\/2020.10.SPINE201354.","journal-title":"J Neurosurg Spine"},{"issue":"5","key":"343_CR5","doi-asserted-by":"publisher","first-page":"E10","DOI":"10.3171\/2018.8.FOCUS18331","volume":"45","author":"N Dietz","year":"2018","unstructured":"Dietz N, Sharma M, Alhourani A, Ugiliweneza B, Wang D, Nuno M, et al. 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. https:\/\/doi.org\/10.3171\/2018.8.FOCUS18331.","journal-title":"Neurosurg Focus"},{"key":"343_CR6","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1177\/21925682221085535","volume":"13","author":"JK Greenberg","year":"2022","unstructured":"Greenberg JK, Landman J, Kelly M, Pennicooke BH, Molina CA, Foraker R, et al. Leveraging artificial intelligence and synthetic data derivatives for spine surgery research. Global Spine J. 2022;13:2409\u201321. https:\/\/doi.org\/10.1177\/21925682221085535.","journal-title":"Global Spine J"},{"key":"343_CR7","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.48078","volume":"15","author":"T Tragaris","year":"2023","unstructured":"Tragaris T, Benetos IS, Vlamis J, Pneumaticos S. Machine learning applications in spine surgery. Cureus. 2023;15: e48078. https:\/\/doi.org\/10.7759\/cureus.48078.","journal-title":"Cureus"},{"issue":"6","key":"343_CR8","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1001\/jamacardio.2021.0122","volume":"6","author":"R Khera","year":"2021","unstructured":"Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633\u201341. https:\/\/doi.org\/10.1001\/jamacardio.2021.0122.","journal-title":"JAMA Cardiol"},{"key":"343_CR9","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jss.2021.02.045","volume":"264","author":"O Elfanagely","year":"2021","unstructured":"Elfanagely O, Toyoda Y, Othman S, Mellia JA, Basta M, Liu T, et al. Machine learning and surgical outcomes prediction: a systematic review. J Surg Res. 2021;264:346\u201361. https:\/\/doi.org\/10.1016\/j.jss.2021.02.045.","journal-title":"J Surg Res"},{"key":"343_CR10","volume-title":"Total hip arthroplasty: medical and biomedical advances","author":"E Tokg\u00f6z","year":"2022","unstructured":"Tokg\u00f6z E. Artificial intelligence, deep learning, and machine learning applications in total hip arthroplasty. In: Total hip arthroplasty: medical and biomedical advances. Springer; 2022."},{"key":"343_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2020.105718","volume":"192","author":"BS Hopkins","year":"2020","unstructured":"Hopkins BS, Mazmudar A, Driscoll CB, Svet MT, Goergen JA, Kelsten MF, et al. Using artificial intelligence (AI) to predict postoperative surgical site infection: a retrospective cohort of 4046 posterior spinal fusions. Clin Neurol Neurosurg. 2020;192: 105718. https:\/\/doi.org\/10.1016\/j.clineuro.2020.105718.","journal-title":"Clin Neurol Neurosurg"},{"key":"343_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/J.SEMSS.2021.100872","volume":"33","author":"PT Ogink","year":"2021","unstructured":"Ogink PT, Groot O, Bindels BJJ, Tobert D. The use of machine learning prediction models in spinal surgical outcome: an overview of current development and external validation studies. Semin Spine Surg. 2021;33: 100872. https:\/\/doi.org\/10.1016\/J.SEMSS.2021.100872.","journal-title":"Semin Spine Surg"},{"key":"343_CR13","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1177\/2192568220967643","volume":"12","author":"C Pedersen","year":"2020","unstructured":"Pedersen C, Andersen M, Carreon L, Eiskj\u00e6r S. Applied machine learning for spine surgeons: predicting outcome for patients undergoing treatment for lumbar disc herniation using PRO data. Global Spine J. 2020;12:866\u201376. https:\/\/doi.org\/10.1177\/2192568220967643.","journal-title":"Global Spine J"},{"key":"343_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/ICMEW.2019.0-112","volume":"2019","author":"M Hoda","year":"2019","unstructured":"Hoda M, El Saddik A, Wai E, Phan P. Predicting spine surgery complications using machine learning. IEEE Int Conf Multimed Expo Workshops (ICMEW). 2019;2019:49\u201353. https:\/\/doi.org\/10.1109\/ICMEW.2019.0-112.","journal-title":"IEEE Int Conf Multimed Expo Workshops (ICMEW)"},{"issue":"1","key":"343_CR15","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1080\/07853890.2022.2059557","volume":"54","author":"S Wolf","year":"2022","unstructured":"Wolf S, Holm SE, Ingwersen T, Bartling C, Bender G, Birke G, et al. Pre-stroke socioeconomic status predicts upper limb motor recovery after inpatient neurorehabilitation. Ann Med. 2022;54(1):1265\u201376. https:\/\/doi.org\/10.1080\/07853890.2022.2059557.","journal-title":"Ann Med"},{"key":"343_CR16","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2697841","author":"C Xiong","year":"2022","unstructured":"Xiong C, Zhao R, Xu J, Liang H, Zhang C, Hui Zhao Z, et al. 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. https:\/\/doi.org\/10.1155\/2022\/2697841.","journal-title":"Comput Math Methods Med"},{"key":"343_CR17","doi-asserted-by":"publisher","DOI":"10.22214\/ijraset.2023.54074","author":"N Sigeef","year":"2023","unstructured":"Sigeef N. An oversampling algorithm combining SMOTE and RF for imbalanced medical data. Int J Res Appl Sci Eng Technol. 2023. https:\/\/doi.org\/10.22214\/ijraset.2023.54074.","journal-title":"Int J Res Appl Sci Eng Technol"},{"key":"343_CR18","doi-asserted-by":"publisher","first-page":"10757","DOI":"10.3934\/mbe.2023477","volume":"20","author":"C Yang","year":"2023","unstructured":"Yang C. Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost. Math Biosci Eng: MBE. 2023;20:10757\u201372. https:\/\/doi.org\/10.3934\/mbe.2023477.","journal-title":"Math Biosci Eng: MBE"},{"key":"343_CR19","doi-asserted-by":"crossref","unstructured":"Ben\u00edtez-Andrades JA, Ord\u00e1s-Reyes N, Garc\u00eda AS, Blanco ME, Nicol\u00e1s JB, Guti\u00e9rrez JV, et\u00a0al. Machine learning in predicting the success of spine surgery: a multivariable study. In: 2024 IEEE 37th international symposium on computer-based medical systems (CBMS);. p. 303\u2013308. ISSN: 2372-9198. Available from: https:\/\/ieeexplore.ieee.org\/document\/10600965.","DOI":"10.1109\/CBMS61543.2024.00057"},{"key":"343_CR20","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3683065","author":"I Rodrigues","year":"2020","unstructured":"Rodrigues I, Parayil A, Shetty T, Mirza I. Use of linear discriminant analysis (LDA), K nearest neighbours (KNN), decision tree (CART), random forest (RF), Gaussian naive bayes (NB), support vector machines (SVM) to predict admission for post graduation courses. Soc Sci Res Netw. 2020. https:\/\/doi.org\/10.2139\/ssrn.3683065.","journal-title":"Soc Sci Res Netw"},{"key":"343_CR21","doi-asserted-by":"publisher","DOI":"10.24940\/ijird\/2022\/v11\/i10\/oct22020","author":"H Adam","year":"2022","unstructured":"Adam H, Muhammad A, Aboaba A. Design of a hybrid machine learning base-classifiers for software defect prediction. Int J Innov Res Dev. 2022. https:\/\/doi.org\/10.24940\/ijird\/2022\/v11\/i10\/oct22020.","journal-title":"Int J Innov Res Dev"},{"key":"343_CR22","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/s42979-020-00296-8","volume":"1","author":"V Chaurasia","year":"2020","unstructured":"Chaurasia V, Pal S. Applications of machine learning techniques to predict diagnostic breast cancer. SN Comput Sci. 2020;1:270. https:\/\/doi.org\/10.1007\/s42979-020-00296-8.","journal-title":"SN Comput Sci"},{"key":"343_CR23","doi-asserted-by":"publisher","first-page":"46","DOI":"10.14419\/IJET.V10I1.31310","volume":"10","author":"MW Yousef","year":"2021","unstructured":"Yousef MW, Batiha K. Heart disease prediction model using Na\u00efve Bayes algorithm and machine learning techniques. Int J Eng Technol. 2021;10:46\u201356. https:\/\/doi.org\/10.14419\/IJET.V10I1.31310.","journal-title":"Int J Eng Technol"},{"key":"343_CR24","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5556992","author":"Y Xiong","year":"2021","unstructured":"Xiong Y, Ye M, Wu C. Cancer classification with a cost-sensitive naive bayes stacking ensemble. Comput Math Methods Med. 2021. https:\/\/doi.org\/10.1155\/2021\/5556992.","journal-title":"Comput Math Methods Med"},{"key":"343_CR25","unstructured":"Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence. 2001;3(22):41\u201346."},{"key":"343_CR26","first-page":"616","volume":"3","author":"JD Rennie","year":"2003","unstructured":"Rennie JD, Shih L, Teevan J, Karger DR. Tackling the poor assumptions of naive Bayes text classifiers. ICML. 2003;3:616\u201323.","journal-title":"ICML"},{"issue":"3","key":"343_CR27","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46(3):175\u201385. https:\/\/doi.org\/10.1080\/00031305.1992.10475879.","journal-title":"Am Stat"},{"key":"343_CR28","doi-asserted-by":"crossref","unstructured":"Salzberg SL. C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. vol.\u00a016. Springer;. Number: 3. Available from: https:\/\/link.springer.com\/article\/10.1007\/BF00993309.","DOI":"10.1023\/A:1022645310020"},{"key":"343_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"343_CR30","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018;106:249\u201359.","journal-title":"Neural Netw"},{"key":"343_CR31","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jss.2021.02.045","volume":"264","author":"O Elfanagely","year":"2021","unstructured":"Elfanagely O, Toyoda Y, Othman S, Mellia J, Basta M, Liu T, et al. Machine learning and surgical outcomes prediction: a systematic review. J Surg Res. 2021;264:346\u201361. https:\/\/doi.org\/10.1016\/j.jss.2021.02.045.","journal-title":"J Surg Res"},{"key":"343_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-81188-6","author":"CM Zhou","year":"2021","unstructured":"Zhou CM, Hong HuJ, Wang Y, Ji M, Hua Tong J, Yang J, et al. A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation. Sci Rep. 2021. https:\/\/doi.org\/10.1038\/s41598-021-81188-6.","journal-title":"Sci Rep"},{"key":"343_CR33","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.artmed.2016.12.003","volume":"75","author":"T Shaikhina","year":"2017","unstructured":"Shaikhina T, Khovanova NA. Handling limited datasets with neural networks in medical applications: a small-data approach. Artif Intell Med. 2017;75:51\u201363. https:\/\/doi.org\/10.1016\/j.artmed.2016.12.003.","journal-title":"Artif Intell Med"},{"key":"343_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40779-021-00338-z","volume":"8","author":"WT Wu","year":"2021","unstructured":"Wu WT, Li Y, Feng A, Li L, Huang T, Xu A, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res. 2021;8:1\u201314. https:\/\/doi.org\/10.1186\/s40779-021-00338-z.","journal-title":"Mil Med Res"},{"key":"343_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104274","volume":"137","author":"K Blagec","year":"2022","unstructured":"Blagec K, Kraiger J, Fr\u00fchwirt W, Samwald M. Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals. J Biomed Inform. 2022;137: 104274. https:\/\/doi.org\/10.1016\/j.jbi.2022.104274.","journal-title":"J Biomed Inform"},{"issue":"1","key":"343_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00385-8","volume":"8","author":"S Alelyani","year":"2020","unstructured":"Alelyani S. Stable bagging feature selection on medical data. J Big Data. 2020;8(1):1\u201318. https:\/\/doi.org\/10.1186\/s40537-020-00385-8.","journal-title":"J Big Data"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00343-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-025-00343-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00343-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T09:08:08Z","timestamp":1765444088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-025-00343-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["343"],"URL":"https:\/\/doi.org\/10.1007\/s13755-025-00343-9","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]},"assertion":[{"value":"27 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"24"}}