用临床病理参数对乳腺癌亚组分类进行火-SVM预测模型
Suvobrata Sarkar1, Kalyani Mali2
1Department of Computer Science and Engineering, Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India.
Digital health
|October 20, 2023
概括
一种新的火虫支持载体机器 (SVM) 模型使用临床病理学数据准确地分类乳腺癌亚型. 这种机器学习方法为三阴性乳腺癌检测提供了更好的预测准确性.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 乳腺癌是女性普遍患的疾病,具有不同的生物和临床特征.
- 机器学习 (ML) 为分析乳腺癌检测和预后的复杂医疗数据提供了潜力.
研究的目的:
- 开发一个数据驱动的乳腺癌模型,用于增强亚型分类.
- 准确区分三阴性乳腺癌 (TNBC) 和非三阴性乳腺癌 (非TNBC).
主要方法:
- 提出了一种用于乳腺癌预测的火虫支持向量机 (SVM) 模型.
- 利用来自三级癌症中心的临床病理学和人口统计数据.
- 采用超参数调整来优化SVM模型.
主要成果:
- 与Grid-SVM,PSO-SVM和GA-SVM相比,火虫-SVM模型实现了更高的预测准确度 (93.4%).
- 模型性能使用F1得分,MSE,AUC,日志损失和PR曲线等指标进行验证.
结论:
- 火-SVM模型是乳腺癌亚组分类的一个有价值的工具.
- 这种方法可以帮助临床医生在患者管理和治疗决策中.
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