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arXiv · 2503.18996

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·Antonio Serrano-García
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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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José Alberto Benítez-Andrades, Camino Prada-García, Nicolás Ordás-Reyes, Marta Esteban Blanco, Alicia Merayo, Antonio Serrano-García. 2025-03-23. Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods. https://doi.org/10.1007/s13755-025-00343-9

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