机器学习算法的比较,用于脊髓瘤的分类
Sheetal Garg1, Bhagyashree Raghavan2
1Department of Electronics & Communication Engineering, ATME College of Engineering, Mysuru, India. sugarg02@gmail.com.
Irish journal of medical science
|August 18, 2023
概括
机器学习 (ML) 模型可以帮助早期诊断和预后脊髓瘤. 本研究探讨了监督的ML技术,用于预测癌症进展和改善临床决策.
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:
- 脊髓瘤是异质的,需要早期诊断和预后,以有效的患者管理.
- 将瘤分类为良性或恶性至关重要,推动研究进入先进的计算方法.
研究的目的:
- 讨论使用监督机器学习技术对脊髓瘤的预测模型.
- 探索机器学习在模拟癌症进展和治疗中的应用.
主要方法:
- 应用各种监督机器学习技术,包括物流回归,支持矢量机器 (SVM),决策树 (DTs) 和随机森林分类器 (RF).
- 使用机器学习工具来检测复杂数据集中的关键特征,用于预测建模.
主要成果:
- 机器学习方法在开发癌症研究预测模型方面已经证明了其有效性和准确性.
- 这些技术对于在复杂的生物数据中识别关键模式是有价值的.
结论:
- 监督机器学习提供了一种有前途的方法,可以提高对脊髓瘤进展的理解和预测.
- 这些机器学习模型在癌症护理中的常规临床实施需要进一步验证.
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