在使用机器学习的儿童中预测行为结果
Samir P V1, Aruna Kumari G2, Nandini Biradar3
1Department of Pedodontics & Preventive Dentistry, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar-751006, India.
Bioinformation
|September 22, 2025
概括
在牙科中预测孩子的行为是很困难的. 机器学习,特别是随机森林,使用年龄和家长焦虑等因素准确预测行为,帮助计划牙科治疗.
科学领域:
- 儿科牙科 儿科牙科
- 机器学习应用 机器学习应用
- 行为科学 行为科学
背景情况:
- 有效的行为管理对于成功的儿科牙科治疗至关重要.
- 预测儿童在牙医诊所期间的合作仍然是一个重大的临床挑战.
研究的目的:
- 评估机器学习模型在预测牙医预约期间儿童行为的有效性.
- 确定影响儿科牙科患者行为的主要临床和历史因素.
主要方法:
- 分析了120名儿童 (4-10岁) 的数据集.
- 包括Random Forest在内的机器学习模型使用年龄,牙科病史和父母焦虑等变量进行训练.
- 用弗兰克尔尺度评估行为.
主要成果:
- 随机森林模型在行为方面显示了最高的预测准确率,为87.5%.
- 显著的负面行为预测因素包括年轻的年龄,父母的高度焦虑,以及以前的负面牙科经验.
- 该模型成功地确定了可能表现出不合作行为的儿童.
结论:
- 机器学习提供了一个有前途的工具,用于预测和管理儿童牙科的行为.
- 早期识别高风险儿童可以促进量身定制的行为指导策略.
- 整合预测模型可以增强治疗规划,改善儿科牙科护理的临床结果.
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