arXiv Open Access 2025

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 +1 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.LG cs.AI

Penulis (6)

José Alberto Benítez-Andrades

Camino Prada-García

Nicolás Ordás-Reyes

Marta Esteban Blanco

Alicia Merayo

Antonio Serrano-García

Format Sitasi

APA MLA BibTeX

Benítez-Andrades, J.A., Prada-García, C., Ordás-Reyes, N., Blanco, M.E., Merayo, A., Serrano-García, A. (2025). Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods. https://arxiv.org/abs/2503.18996

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓