对骨质疏松症预测模型进行全面的分析和绩效评估
Zahraa Noor Aldeen M Shams Alden1,2, Oguz Ata3
1Faculty of Tourism Science, University of Kerbala, Kerbala, Iraq.
PeerJ. Computer science
|February 3, 2025
概括
深度学习使用NHANES数据准确预测骨质疏松症. 与CNN模型的相互信息功能选择实现了99%以上的准确性,识别了家庭病史和药物使用等关键风险因素.
科学领域:
- 医学数据分析和计算健康.
- 人工智能在医疗保健诊断中的应用.
背景情况:
- 医疗数据分析为医疗保健提供了变革性的潜力.
- 利用研究数据可以提高临床决策和患者的治疗结果.
研究的目的:
- 使用深度学习技术预测骨质疏松症的发病.
- 评估深度神经网络模型的特征选择方法 (相互信息和递归特征消除).
主要方法:
- 使用了NHANES 2017-2020数据集,预处理为脊椎骨和骨骨数据集.
- 应用了顺序深度神经网络,卷积神经网络 (CNN) 和循环神经网络.
- 员工相互信息 (MI) 和递归特征消除 (RFE) 用于特征选择.
主要成果:
- 在精度上,相互信息 (MI) 胜过递归特征消除 (RFE).
- 由MI选择的CNN模型在脊椎骨方面达到99.15%的准确率,而在腿骨骨方面达到99.94%.
- 确定了重要的预测因素:家庭病史,患者骨折,父母关节骨折,以及定期使用普得尼松或皮质松.
结论:
- 深度学习,特别是CNN与MI特征选择,在从非图像医疗数据中预测骨质疏松症方面表现出高效.
- 这些发现支持为医疗保健提供者增强诊断和预后模型.
- 强调在骨质疏松风险评估中,特定的临床和家族因素的重要性.
关键词:
分类 分类 分类 分类.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.功能选择 功能选择互助信息 (MI) 是指互助的信息.非图像医疗数据的医疗数据经常性神经网络 (RNNs) 是一种神经网络.递归特征消除 (RFE) 是一种方法.更多相关视频
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