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不同的机器学习方法基于大鼻在西北中国汉族人口的性别估计
Yu-Xin Guo1,2, Jun-Long Lan1, Yu-Xuan Song3
1Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an, Shaanxi, 710004, People's Republic of China.
International journal of legal medicine
|May 17, 2024
概括
maksillary sinus测量为18岁以上的成年人提供了一种可靠的性别估计方法. 随机森林模型,结合年龄,在法医识别中实现了88.46%的准确性.
科学领域:
- 法医人类学 法医人类学
- 医学成像分析 医学成像分析
- 机器学习在法医中
背景情况:
- 性估计在法医科学中至关重要.
- 鼻是适合法医分析的弹性解剖结构.
- 机器学习提高了从解剖学数据中预测性别的准确性.
研究的目的:
- 为了评估上鼻线性测量对性别估计的有效性.
- 开发和比较机器学习模型 (物流,KNN,SVM,RF) 用于性别预测.
- 评估年龄对性别估计准确性的影响.
主要方法:
- 收集了来自477个人 (汉族人口,中国西北部) 的圆束计算机断层扫描 (CBCT) 数据.
- 测量了 sinus 的线性尺寸 (长度,宽度,高度) 和 sinus 间的距离.
- 开发并验证了后勤,KNN,SVM和随机森林模型,使用80%的培训和20%的测试数据分割.
主要成果:
- 随机森林模型在18岁以上的个体中实现了77.78%的整体准确性.
- 男人 (78.12%) 的精度略高于女性 (77.42%).
- 将年龄作为一个变量提高了准确性,在18-27岁年龄组达到88.46%,所有变量与年龄呈现线性相关性.
结论:
- 线性上鼻子测量对于成年人 (≥18岁) 的性别估计有价值.
- 一个强大的随机森林模型被开发用于中国西北部汉族人口的性别估计.
- 年龄是提高性别估计准确性的重要预测变量.
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