基于广度优先搜索算法的骨科疾病分类
Ahmed M Elshewey1, Ahmed M Osman2
1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt.
Scientific reports
|October 7, 2024
概括
这项研究通过优化机器学习模型来增强骨科疾病诊断. 二元宽度首次搜索 (BBFS) 与随机森林 (RF) 结合,实现了99.41%的准确性,显著提高了诊断能力.
科学领域:
- 整形外科 整形外科 整形外科
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
背景情况:
- 骨科疾病在全球普遍存在,影响肌肉骨系统,导致疼痛和功能障碍.
- 目前用于骨科疾病的诊断方法可能缺乏足够的补充工具.
- 需要改进和准确的方法来诊断骨科疾病.
研究的目的:
- 为了解决骨科疾病的补充诊断方面的局限性.
- 通过先进的计算技术,提高诊断骨科疾病的方法.
- 评估各种特征选择和机器学习模型在骨科诊断中的有效性.
主要方法:
- 使用二进制宽度优先搜索 (BBFS),二进制粒子群优化 (BPSO),二进制灰狼优化器 (BGWO) 和二进制优化算法 (BWAO) 进行了特征选择.
- 应用了六种机器学习模型,包括随机森林 (RF),支持粒度下降 (SGD),天真贝叶斯分类器 (NBC),区分分析 (DC),二次区分分析 (QDA) 和额外树木 (ET).
- 使用BBFS,BPSO,BGWO和BWAO进一步优化了射频模型的参数,使用准确度,灵敏度,特异性,F-score和AUC曲线来评估性能.
主要成果:
- 与其他特征选择算法相比,BBFS的平均错误减少率为47.29%.
- 随机森林 (RF) 模型最初在测试的机器学习模型中实现了91.4%的准确性.
- 优化的BBFS-RF模型显著提高了性能,在数据集上达到99.41%的准确性.
结论:
- 整合BBFS用于特征选择和RF用于分类,为诊断骨科疾病提供了一个高度准确的方法.
- 优化的机器学习模型,特别是BBFS-RF,在提高骨科诊断的准确性和可靠性方面显示出巨大的前景.
- 这项研究提供了一个强大的计算框架,可以通过更早,更精确的疾病检测来改善患者的治疗结果.
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