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Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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使用增强机器学习管道和可解释AI的肺癌风险预测.

Pavithran M S¹, Saranyaraj D¹, Anirban Chakrabortty¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu, India.

Frontiers in artificial intelligence

|September 19, 2025

概括

用于肺癌预测的机器学习模型与不平衡的数据作斗争. 采用多层感知子分类器的K-Means SMOTE增强实现了93.55%的准确性,改善了肺癌风险预测.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
计算机科学计算机科学

背景情况:

肺癌是全球癌症死亡的主要原因,需要提高诊断准确度.
机器学习 (ML) 显示了肺癌预测的潜力,但临床数据集经常遭受类不平衡,阻碍了分类器的性能.
阶级不平衡导致偏见的预测和医学诊断的ML模型的精度降低.

研究的目的:

用ML评估各种数据增强技术在解决肺癌预测阶级不平衡方面的有效性.
为了比较不同增强-分类器组合在小,不平衡的肺癌数据集上的性能.
提高基于ML的肺癌风险预测模型的准确性和可靠性.

主要方法:

将几种数据增强技术应用于具有显著类不平衡的小型肺癌数据集.
训练和评估各种ML分类器与这些增强方法相结合.
使用K-Means SMOTE与多层感知器 (MLP) 分类器相结合进行比较分析.
使用LIME (局部可解释的模型不可知解释) 来实现模型的可解释性.

主要成果:

结合K-Means SMOTE数据增强和多层感知子分类器,获得了最高的性能.
获得了93.55%的分类准确率和96.76%的接收器操作特征曲线 (AUC-ROC) 下面面积得分.

关键词:

在SMOTE中使用.阶级不平衡阶级不平衡可以解释的人工智能AI 石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰,石灰肺癌的预测肺癌的预测

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这种增强-分类器对显著优于其他测试的组合,证明了优化数据增强的影响.

结论:

数据增强技术对于缓解基于ML的医学预测任务中的类失衡问题至关重要.
K-Means SMOTE和MLP组合是一种有前途的方法,可以提高肺癌风险预测的准确性.
用更大,更具代表性的数据集进行进一步验证是有必要的,以将这些发现转化为临床工具.