随机树算法分析创伤性牙损伤类型与其人口统计和倾向因素之间的关系 - 一项横截面研究
Mohammad Kamran Khan1, Mahendra Kumar Jindal1
1Department of Pediatric and Preventive Dentistry, Faculty of Medicine, Aligarh Muslim University (AMU), Aligarh, Uttar Pradesh, India.
概括
诸如年龄,性别和地理区域等人口因素,以及关节关系,是儿童创伤性牙损伤 (TDI) 的关键预测因素. 机器学习确定这些在确定伤害类型方面最有影响力.
科学领域:
- 儿科牙科 儿科牙科
- 牙科公共卫生 牙科公共卫生
- 人工智能在医学中的应用
背景情况:
- 创伤性牙损伤 (TDI) 在儿童和青少年中是一个重要的全球公共卫生问题.
- TDIs 的病因是复杂的和多因素的,需要先进的分析方法.
研究的目的:
- 分析创伤性牙损伤 (TDI) 类型与儿童人口统计学/倾向因素之间的关联.
- 应用机器学习 (ML) 技术来识别TDI的关键预测因素.
主要方法:
- 对来自随机选择的学校的12岁和15岁儿童进行了一项观测横截面研究.
- 通过结构化采访和牙科检查收集了TDI的数据,并使用IBM SPSS Modeler中的随机树模型进行分析.
- 机器学习 (ML) 和人工智能 (AI) 用于高级统计分析.
主要成果:
- 随机树模型确定了摩尔关系 (2.5),年龄 (1.75),性别 (1.5) 和地理区域 (~1.5) 作为TDI类型最重要的预测因素.
- 临床因素,如过度喷射 (0.75),唇部能力 (0.5) 和过度咬伤 (0.5) 在确定TDI类型方面的重要性较低.
结论:
- 人口因素 (年龄,性别,地理区域) 和关节关系是儿童创伤性牙损伤类型的最强预测因素.
- 机器学习有效地确定了影响TDI的关键因素,有助于理解其病因.
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