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

• URL: http://arxiv.org/abs/2503.18996v1
• Date: Sun, 23 Mar 2025 22:39:19 GMT
• Title: Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods
• Authors: José Alberto Benítez-Andrades, Camino Prada-García, Nicolás Ordás-Reyes, Marta Esteban Blanco, Alicia Merayo, Antonio Serrano-García,
• Abstract summary: The study proposes an advanced machine learning approach to predict spine surgery outcomes by incorporating oversampling techniques and grid search optimization.<n>The enhanced KNN models achieved up to 76% accuracy and a 67% F1-score, while grid-search optimization further improved performance.
• Score: 0.32985979395737786
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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