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解码儿童牙健康风险:一种机器学习方法来识别关键影响因素
Seyed-Ali Sadegh-Zadeh1, Mahshid Bagheri2, Mozafar Saadat3
1Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom.
机器学习识别了幼儿牙损伤的关键风险因素. 不良的口腔卫生,含糖饮食和低化物暴露显著增加风险,使有针对性的预防策略成为可能.
科学领域:
- 儿科牙科 儿科牙科
- 在医疗保健中的数据科学.
- 公共卫生 公共卫生
背景情况:
- 牙损是儿童早期的一个普遍问题.
- 早期识别高风险儿童对于有效预防至关重要.
- 了解影响因素有助于制定有针对性的干预措施.
研究的目的:
- 调查影响7岁及以下儿童牙风险的关键因素.
- 通过机器学习增强对高风险个体的早期识别和预防策略.
主要方法:
- 对356名儿童的临床检查数据的分析.
- 机器学习模型的应用:物流回归,决策树和随机森林.
- 评估饮食习惯,化物暴露和社会经济地位.
主要成果:
- 糟糕的口腔卫生,高糖饮食和低化物暴露被确定为重要的危险因素.
- 随机森林模型在风险评估方面表现出卓越的表现.
- SHAP分析证实了卫生,饮食和化物的影响.
结论:
- 机器学习可以有效地识别和量化儿童牙的危险因素.
- 这种方法支持有针对性的干预和预防措施.
- 通过数据驱动的策略,促进改善儿科牙科健康结果.
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