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

arXiv: https://arxiv.org/abs/2503.18996 · HTML: https://mlsanity.com/paper/2503.18996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals](https://mlsanity.com/paper/2201.07040/) (2022)

## Abstract (arXiv)

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.