使用机器学习的早期儿童 (ECC) 预测模型
Daniel José Blanco-Victorio1, Roxana Patricia López-Ramos2, Johan Daniel Blanco-Rodriguez3
1Facultad de Ciencias e Ingeniería Universidad Peruana Cayetano Heredia Lima. Perú.
Journal of clinical and experimental dentistry
|January 17, 2025
概括
机器学习模型有效地预测了儿童早期 (ECC). 支持矢量机器和神经网络在识别患有的儿童方面表现出卓越的表现,有助于早期诊断和干预.
科学领域:
- 牙科 牙科是指牙科的专业.
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 幼儿 (ECC) 是一个重要的公共卫生问题.
- 预测模型可以帮助早期诊断和预防策略.
- 了解机器学习的性能对于ECC预测至关重要.
研究的目的:
- 评估机器学习模型,用于预测幼儿.
- 用关键指标比较不同预测模型的性能.
主要方法:
- 一项横截面研究分析了3-6岁的186名儿童的数据.
- 使用色数据挖掘软件应用了机器学习模型.
- 使用精度,回忆,F1得分,准确性和ROC曲线来评估模型性能.
主要成果:
- 76.88%的儿童出现了.
- 支持矢量机 (SVM) 和神经网络 (NN) 模型显示出最高的性能.
- SVM和NN实现了高精度 (0.927) 和回忆 (0.974).
结论:
- 机器学习模型,特别是SVM和NN,对于虫预测是有效的.
- 这些人工智能驱动的工具对识别患有ECC风险的儿童有很大的希望.
- 进一步的研究可以完善这些模型用于临床应用.
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