基于SMOTE-RFE-XGBoost模型的脊髓疾病的分类和预测
Biao Zhang1, Xinyan Dong2, Yuwei Hu2
1School of Computer Science, Liaocheng University, Liaocheng, Shandong, China.
PeerJ. Computer science
|June 22, 2023
概括
一个新的SMOTE-RFE-XGBoost模型使用骨角准确地预测脊髓疾病. 这种方法通过克服数据不平衡和提高分类准确性来改善患者的治疗结果,从而提高了诊断.
科学领域:
- 整形外科和医疗信息学
- 生物医学工程 生物医学工程
- 在医疗保健中的数据科学.
背景情况:
- 脊椎疾病带来复杂的诊断挑战,通常依赖于主观的临床经验.
- 目前用于脊柱疾病的诊断方法需要显著提高准确性和一致性.
研究的目的:
- 开发一个客观的,数据驱动的模型来预测脊髓疾病.
- 通过先进的机器学习技术,提高脊柱疾病诊断的准确性.
主要方法:
- 使用合成少数群体过量采样技术 (SMOTE) 来解决脊柱状况数据中的类不平衡.
- 使用递归特征消除 (RFE) 来从物理骨角指标中进行强大的特征选择.
- 开发和评估了一个与SMOTE和RFE集成的极端梯度增强 (XGBoost) 分类模型.
主要成果:
- 拟议的SMOTE-RFE-XGBoost模型实现了97.56%的高分类精度.
- 该模型显示了0.1111的平均平方误差 (MSE) 和0.8696.6的F1得分.
- 确定了关键的预测特征,包括腰部滑动,宫倾斜,骨盆半径和骨盆倾斜.
结论:
- SMOTE-RFE-XGBoost模型提供了一个非常准确和可靠的方法来预测脊髓疾病.
- 这种方法标志着客观脊柱疾病诊断的突破,超越了传统的临床经验.
- 确定的关键指标为了解脊柱生物力学和疾病进展提供了有价值的见解.
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