优化皮肤癌存活率预测使用合奏技术
Erum Yousef Abbasi1, Zhongliang Deng1, Arif Hussain Magsi2
1State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Bioengineering (Basel, Switzerland)
|January 22, 2024
概括
这项研究引入了先进的机器学习组合方法,用于准确地预测皮肤皮肤黑色素瘤 (SKCM) 癌症和生存分析. 随机森林分类器实现了99%的准确性,大大提高了诊断能力.
科学领域:
- 计算生物学和生物信息学
- 人工智能在瘤学中的应用
- 高通量数据分析的数据分析.
背景情况:
- 癌症研究正在推进高通量技术和人工智能 (AI) 以改善诊断和向治疗.
- 复杂,不平衡和高维数据对瘤学中的计算方法和多omics分析提出了重大挑战.
- 准确的预测和生存分析对于有效的皮肤皮肤黑色素瘤 (SKCM) 管理至关重要.
研究的目的:
- 开发和评估基于机器学习 (ML) 的整体方法,用于预测皮肤癌 (SKCM) 和分析整体生存概率.
- 解决高维和不平衡数据集在计算癌症研究中的挑战.
主要方法:
- 利用ICGC数据门户网站上公开提供的皮肤皮肤黑色素瘤 (SKCM) 数据集.
- 采用了八个基线分类器:随机森林 (RF),决策树 (DT),梯度增强 (GB),AdaBoost,高斯素朴贝叶斯 (GNB),额外树 (ET),后勤回归 (LR) 和光梯度增强机 (LGBM).
- 开发和评估了四种组合方法 (堆叠,袋装,提升,投票) 以及使用卡普兰-梅尔和考克斯比例危险回归的生存分析.
主要成果:
- 与传统方法相比,拟议的基于ML的整体方法表现出优异的性能.
- 随机森林 (RF) 分类器实现了卓越的精度,而投票组合方法达到99%的准确性.
- 射频分类器还实现了优异的99%准确度,这表明其在SKCM预测方面的卓越性能.
结论:
- 开发的ML组合技术显著提高了皮肤皮肤黑色素瘤 (SKCM) 诊断的准确性.
- 随机森林分类器和投票组合方法在SKCM中显示出临床应用的巨大潜力.
- 这项研究有助于更准确,更有效的SKCM诊断,超过现有的最先进的技术.
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