通过人工神经网络模型和弹性净回归来预测乳腺癌患者的死亡率
Anis Esmaeili1, Ali Karamoozian1, Abbas Bahrampour1,2
1Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
Journal of research in health sciences
|February 25, 2025
概括
这项研究比较了弹性净回归和人工神经网络 (ANN) 来预测乳腺癌死亡率. 两种模型都确定了关键因素,ANN显示了更高的灵敏度和弹性网,为乳腺癌生存预测提供了更好的特异性和准确性.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 乳腺癌 (BC) 是全球妇女死亡的主要原因.
- 准确预测BC死亡率对于患者管理和治疗策略至关重要.
- 确定BC死亡率的预测模型是一个正在进行的研究领域.
研究的目的:
- 评估和比较弹性净回归和人工神经网络 (ANN) 模型在预测乳腺癌死亡率方面的性能.
- 使用这些模型,识别影响乳腺癌死亡率的关键因素.
- 评估机器学习方法在瘤学中的诊断和预后能力.
主要方法:
- 一项横截面研究分析了2,836名乳腺癌患者 (2014-2018) 的数据.
- 利用弹性净回归和人工神经网络 (ANN) 模型来预测死亡率.
- 使用以下指标进行模型性能比较:灵敏度,特异性,精度,AUC,精度和F1分数.
主要成果:
- 弹性净回归实现了0.814的特异性和0.792.79的准确性.
- 人工神经网络 (ANN) 显示了更高的灵敏度 (0.66) 和AUC (0.704).
- 弹性净回归在特异性,准确性,精度和F1得分方面表现优于ANN.
结论:
- 弹性净回归和ANN模型都可以用于预测乳腺癌死亡率.
- ANN模型为预测BC死亡率提供了更高的灵敏度和AUC.
- 弹性净回归提供了更好的特异性和准确性,形态学,瘤分化和年龄被确定为影响死亡率的重要因素.
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