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儿科面创伤:基于机器学习的预测建模,以识别创伤模式.

Elsy Antony1, Saima Yunus Khan1, Md Kalim Ansari2

  • 1Department of Pediatric and Preventive Dentistry, Dr. Ziauddin Ahmad Dental College, Aligarh Muslim University, Aligarh, India.

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此摘要是机器生成的。

机器学习模型准确地预测儿科面部创伤,确定年龄和社会经济因素,如父母的教育和就业,作为关键预测因素. 来自较低社会经济背景的儿童面临这种伤害的风险更高.

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科学领域:

  • 医疗信息学 医疗信息学
  • 儿科创伤学 儿科创伤学
  • 医疗保健中的机器学习

背景情况:

  • 儿科面部创伤在诊断和管理方面存在独特的挑战.
  • 预测建模可以识别有风险的人群,并告知预防策略.

研究的目的:

  • 为了分析儿科面部创伤的特征.
  • 使用机器学习算法预测影响因素.

主要方法:

  • 一项基于医院的前性研究包括患有大面部创伤的儿科患者 (长达15岁).
  • 使用后勤回归,贝叶斯网络,CHAID和神经网络算法分析数据.

主要成果:

  • 贝叶斯网络和物流回归实现了92.59%的准确性.
  • 年龄,父亲的教育,母亲的教育和父母的就业是重要的预测因素.
  • 贝叶斯网络确定了道路交通事故 (RTA) 和性别作为其他关键变量.

结论:

  • 年龄是预测儿科口腔面部创伤的最关键因素.
  • 父母的教育和就业是重要的预测因素,表明较低社会经济群体的风险更高.
  • 机器学习模型在识别儿科面部创伤的风险因素方面具有很高的准确性.