Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods
Journal:
arXiv
Published Date:
Mar 23, 2025
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.
