使用轻量级深度学习模型进行基于SMOTE的自动化PCOS预测.
Rumman Ahmad1, Lamees A Maghrabi2, Ishfaq Ahmad Khaja1
1Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India.
Diagnostics (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了先进的深度学习模型,用于准确预测多囊性卵巢综合征 (PCOS). 基于CNN的模型表现出卓越的性能,为早期PCOS检测和减少流产风险提供了一个有前途的工具.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 生殖健康研究 生殖健康研究
背景情况:
- 多囊卵巢综合征 (PCOS) 显著影响生殖年龄的女性,高水平导致流产和排卵问题.
- 脊髓灰质炎影响着相当一部分人口,最近的研究表明,亚洲女性的患病率高达31.3%.
- 现有的PCOS检测机器学习方法通常依赖于手动特征提取,导致性能限制并阻碍准确的诊断.
研究的目的:
- 开发和评估用于PCOS预测中的自动化特征工程的尖端深度学习模型.
- 通过利用先进的深度学习技术,提高PCOS检测的准确性和性能.
- 解决传统机器学习方法在准确识别PCOS方面的局限性.
主要方法:
- 提出了三个轻量级的深度学习模型:基于LSTM的,基于CNN的和基于CNN-LSTM的.
- 利用合成少数人过量采样技术 (SMOTE) 进行有效的数据集平衡,以确保强大的模型性能.
- 通过深度学习架构实现自动功能提取,消除了手动功能工程的需要.
主要成果:
- 基于CNN的模型实现了最高的准确性 (96.59%) 和ROC-AUC (96.6%),具有最小的参数数量 (297) 和快速训练时间 (10.02秒).
- 使用DeLong测试证实了统计学意义,比较了所有三种模型的AUC.
- 在准确性,精度,回忆力,AUC,参数数量和训练效率方面,SMOTE + CNN模型的表现优于其他模型.
结论:
- 提出的深度学习模型,特别是基于CNN的方法,在PCOS检测方面表现优越,与现有的最先进的方法相比.
- 开发的模型显示了早期PCOS识别的潜力,这可以有助于减少流产等妊娠并发症.
- 这项研究强调了深度学习与自动化特征工程的有效性,以改善PCOS诊断和管理.
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