癌症生存预测的进展:古典和现代方法的系统审查
Gitanjali Pradhani1, Jonali Gogoi1
1Department of Statistics, Assam University, Silchar, Assam, India.
概括
准确预测癌症患者的生存率对于治疗至关重要. 本次审查强调了机器学习和深度学习模型,显示了改进癌症生存预测的先进技术的趋势.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 癌症的不可预测性和缺乏早期症状需要准确的生存预测,以获得更好的治疗结果.
- 传统方法在处理癌症的复杂性方面往往不足,导致需要先进的计算方法.
- 数字化促进了机器学习 (ML) 和深度学习 (DL) 的应用,以提高癌症存活率预测.
研究的目的:
- 确定用于癌症预测的生存分析模型.
- 为突出癌症存活率分析的ML和DL最近的进展.
- 建议该领域的未来研究方向.
主要方法:
- 在ScienceDirect,IEEE Xplore和PubMed.com进行了全面的文献搜索.
- 布尔搜索策略识别了过去15年的相关研究.
- 在选择文章时遵循了PRISMA指南,批判性地检查了51项选定的研究.
主要成果:
- 在癌症存活率分析中,人们观察到从传统的统计方法转向先进的ML技术.
- 越来越多的人使用临床数据来预测存活率,即使数据集有限.
- 混合和深度学习模型显示出越来越多的兴趣和潜力,尽管传统的ML方法仍然普遍存在.
结论:
- 改进的ML方法对于实现更准确的癌症存活预测至关重要.
- 鼓励对深度学习模型进行进一步的研究,以推进癌症生存分析.
- 本综述提供了有关癌症生存预测当前方法和未来研究途径的宝贵见解.
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