孕期孕产妇健康风险分类的深度混合模型:ANN和随机森林的协同作用
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Taofeeq Oluwatosin Togunwa1,2, Abdulhammed Opeyemi Babatunde1,2,3,4, Khalil-Ur-Rahman Abdullah5,6
1Department of Medicine and Surgery, Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Oyo, Nigeria.
Frontiers in artificial intelligence
|July 21, 2023
概括
这项研究引入了一种混合人工智能模型,用于分类怀孕期间母亲的健康风险. 该模型实现了95%的准确性,改善了对母亲和婴儿的早期检测和结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 孕产妇死亡率仍然是一个重大的全球卫生挑战,特别是在发展中国家.
- 尽管取得了进展,但改善孕产妇和胎儿健康需要创新的方法.
- 人工智能 (AI) 为分析复杂的医疗数据提供了新的方法.
研究的目的:
- 开发和评估一种深度混合模型,用于分类怀孕期间母亲的健康风险.
- 提高识别高风险怀孕的准确性和效率.
- 通过更好的风险分层来改善母亲和婴儿的整体福祉.
主要方法:
- 提出了一个集成人工神经网络 (ANN) 和随机森林 (RF) 算法的深度混合模型.
- 该模型利用了一个数据集,包括母亲的特征,如年龄,血压和血糖.
- 使用最大概率投票系统来确定最终的风险分类.
主要成果:
- 混合模型在测试数据集上表现出高性能.
- 实现了95%的准确性,97%的精度,97%的回忆,以及0.97的F1得分.
- 表明对准确的孕产妇风险评估有很大的潜力.
结论:
- 拟议的深度混合模型显示了改善孕产妇健康风险分类的前景.
- 这种方法可以为孕妇和新生儿带来更好的健康结果.
- 未来的工作应侧重于概括性,整合多样化的数据和临床验证.
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