用机器学习提升脊柱手术预后预测准确率
Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods
- 融合过采样与网格搜索优化的KNN模型提升预测效果
- 最高达76%准确率，F1-score达到67%
- 适合医疗决策支持系统开发与临床研究参考
本研究提出一种基于机器学习的脊柱手术预后预测方法，结合过采样技术与网格搜索优化。在包含244名患者的多维度数据集上，测试了GaussianNB、ComplementNB、KNN、决策树及经RandomOverSampler和SMOTE优化的版本。优化后的KNN模型达到最高76%准确率与67% F1-score，网格搜索进一步提升了性能。结果表明，这些先进方法有助于医疗决策支持，未来需在更大更多样本数据集上继续优化。
原文摘要 · Abstract (English)
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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