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.
View on arXiv@article{benítez-andrades2025_2503.18996,
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 },
journal={arXiv preprint arXiv:2503.18996},
year={ 2025 }
}