Certain cancer types, notably pancreatic cancer, are difficult to detect at an early stage, motivating robust biomarker-based screening. Liquid biopsies enable non-invasive monitoring of circulating biomarkers, but typical machine learning pipelines for high-dimensional tabular data (e.g., random forests, SVMs) rely on expensive hyperparameter tuning and can be brittle under class imbalance. We leverage a meta-trained Hyperfast model for classifying cancer, accomplishing the highest AUC of 0.9929 and simultaneously achieving robustness especially on highly imbalanced datasets compared to other ML algorithms in several binary classification tasks (e.g. breast invasive carcinoma; BRCA vs. non-BRCA). We also propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks, achieving an incremental increase in accuracy (0.9464) while merely using 500 PCA features; distinguishable from previous studies where they used more than 2,000 features for similar results. Crucially, we demonstrate robustness under class imbalance: empirically via balanced accuracy and minority-class recall across cancer-vs.-noncancer and cancer-vs.-rest settings, and theoretically by showing (i) a prototype-form final layer for Hyperfast that yields prior-insensitive decisions under bounded bias, and (ii) minority-error reductions for majority vote under mild error diversity. Together, these results indicate that pre-trained tabular models and simple ensembling can deliver state-of-the-art accuracy and improved minority-class performance with far fewer features and no additional tuning.

翻译：某些癌症类型，尤其是胰腺癌，在早期阶段难以检测，这促使了基于生物标志物的鲁棒筛查需求。液体活检能够无创监测循环生物标志物，但典型的高维表格数据机器学习流程（如随机森林、支持向量机）依赖于昂贵的超参数调优，且在类别不平衡情况下可能表现脆弱。我们利用元训练的Hyperfast模型进行癌症分类，在多个二分类任务（例如乳腺浸润性癌；BRCA与非BRCA）中实现了最高的AUC值0.9929，同时与其他机器学习算法相比，尤其在高度不平衡数据集上表现出鲁棒性。我们还提出了一种新颖的集成模型，结合了预训练的Hyperfast模型、XGBoost和LightGBM用于多分类任务，仅使用500个PCA特征就实现了准确率的增量提升（0.9464）；这与先前研究中使用超过2000个特征获得类似结果形成鲜明对比。关键的是，我们证明了模型在类别不平衡下的鲁棒性：通过癌症与非癌症及癌症与其余类别的平衡准确率和少数类召回率进行实证验证，并从理论上展示了（i）Hyperfast的原型形式最终层在有界偏差下产生对先验不敏感的决策，以及（ii）在温和误差多样性下多数投票能减少少数类误差。总之，这些结果表明，预训练的表格模型和简单集成方法能够以更少的特征且无需额外调优，实现最先进的准确率并提升少数类性能。