Certain cancer types, namely pancreatic cancer is difficult to detect at an
early stage; sparking the importance of discovering the causal relationship
between biomarkers and cancer to identify cancer efficiently. By allowing for
the detection and monitoring of specific biomarkers through a non-invasive
method, liquid biopsies enhance the precision and efficacy of medical
interventions, advocating the move towards personalized healthcare. Several
machine learning algorithms such as Random Forest, SVM are utilized for
classification, yet causing inefficiency due to the need for conducting
hyperparameter tuning. 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.

通过非侵入式方法，液体活检能够检测和监测特定生物标志物，提高医疗干预的精确性和效能，利用超快模型进行癌症分类，并结合超快模型、XGBoost 和 LightGBM 提出了一种新的集成模型用于多类别分类任务，仅使用 500 个主成分特征即可实现较高的准确性。