对ARX和ANFIS模型进行比较分析,用于预测单剂和多剂化疗下瘤生长
Sotirios G Liliopoulos1, George S Stavrakakis2, Konstantinos S Dimas3
1School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece.
Anticancer research
|May 31, 2024
概括
数学模型,包括带有外源输入的自回归 (ARX) 和自适应的神经模糊推理系统 (ANFIS),被开发用于研究化疗下瘤生长动态. 安菲斯模型在预测瘤抑制方面表现出卓越的表现,为个性化癌症治疗提供了潜力.
科学领域:
- 在瘤学瘤学.
- 数学生物学 数学生物学
- 计算科学 计算科学
背景情况:
- 癌症仍然是导致死亡的主要原因,尽管治疗方面取得了进展.
- 数学建模为理解复杂的癌症动态提供了一种强大的方法.
研究的目的:
- 开发和评估用于描述化疗下瘤生长的数学模型.
- 为了比较自回归与外源输入 (ARX) 和自适应神经模糊推理系统 (ANFIS) 模型的预测能力.
主要方法:
- 开发并估计了四个ARX和ANFIS模型,使用异种移植小鼠的化疗治疗数据.
- 在单一和组合化疗方案下研究瘤生长动态.
- 利用自适应预测和滑动数据窗口技术,以持续更新模型.
主要成果:
- 两种ARX和ANFIS模型都显示出与瘤体重数据的强烈相关性.
- 在捕捉多剂化疗复杂性方面,ANFIS模型表现出卓越的性能.
- 模型准确预测瘤的生长时间,提前5天,ANFIS显示出更高的可靠性.
- 长期预测准确度下降,突出了目前的局限性.
结论:
- 在化疗中,ANFIS模型为瘤生长预测提供了更高的可靠性.
- ARX模型为即时近似提供了一个更简单,快速部署的替代方案.
- 对于临床决策和个性化治疗方案,建议对更大的数据集和各种复杂模型进行进一步的研究.
关键词:
安菲斯 (ANFIS) 是一个字母.在ARX中,ARX就是ARX.化疗 化疗是一种化学疗法.我们的PDAC是PDAC.在TGI上,它是TGI.适应性神经模糊推理系统适应性瘤生长 短期预测自动回归与外源输入的输入.临床决策 临床决策数学建模的数学建模胰腺管道腺癌瘤抑制瘤生长 抑制瘤生长更多相关视频
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