Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods

José Alberto Benítez-Andrades, Camino Prada-García, Nicolás Ordás-Reyes, Marta Esteban Blanco, Alicia Merayo +1 more
3/23/2025
cs.LGcs.AI

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Abstract

The study proposes an advanced machine learning approach to predict spine surgery outcomes by incorporating oversampling techniques and grid search optimization. A variety of models including GaussianNB, ComplementNB, KNN, Decision Tree, and optimized versions with RandomOverSampler and SMOTE were tested on a dataset of 244 patients, which included pre-surgical, psychometric, socioeconomic, and analytical variables. The enhanced KNN models achieved up to 76% accuracy and a 67% F1-score, while grid-search optimization further improved performance. The findings underscore the potential of these advanced techniques to aid healthcare professionals in decision-making, with future research needed to refine these models on larger and more diverse datasets.

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Cite this paper

@article{bentezandrades2025enhanced,
  title  = {Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods},
  author = {José Alberto Benítez-Andrades and Camino Prada-García and Nicolás Ordás-Reyes and Marta Esteban Blanco and Alicia Merayo and Antonio Serrano-García},
  year   = {2025},
  eprint = {2503.18996},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.18996v1}
}

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