使用显著特征和多模型分类器提高牙损伤的预测
1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PeerJ. Computer science
|December 11, 2023
概括
这项研究引入了一种基于特征的新方法来检测牙 (牙损伤). 拟议的方法,利用原理组件分析 (PCA) 和投票分类器,显著提高诊断准确性.
科学领域:
- 牙科医学 牙科医学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 牙腐烂 (牙腐烂) 是一个普遍的口腔健康问题,需要早期诊断.
- 这种疾病涉及由于细菌和糖分导致牙组织的分解.
- 现有的基于图像的检测方法已经显示出局限性.
研究的目的:
- 开发和评估一种新的基于特征的方法,用于增强牙腐烂检测.
- 为了利用原理组件分析 (PCA) 和Chi-square (chi2) 进行特征工程.
- 评估投票分类器组合用于虫检测的有效性.
主要方法:
- 使用基于特征的数据集,而不是基于图像的数据集.
- 用于特征生成的使用原理组件分析 (PCA).
- 实现了一个投票分类器组合,包括极端梯度提升 (XGB),随机森林 (RF) 和额外树木分类器 (ETC).
主要成果:
- 具有PCA特征的拟议投票分类器实现了高性能指标.
- 获得的准确率为97.36%,精度为96.14%,回忆率为96.84%,F1得分为96.65%.
- 使用 chi2 特性和其他机器学习模型的超越性能的方法.
结论:
- 基于特征的数据集与PCA和投票分类器组合显著提高了牙腐烂检测的准确性.
- 该模型显示了有效诊断牙损伤的强大潜力.
- 这项研究提供了通过改进的诊断方法来推进牙科保健的创新见解.
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