Survival Prediction from Imbalance colorectal cancer dataset using
  hybrid sampling methods and tree-based classifiers
Sadegh Soleimani Mahsa Bahrami Mansour Vali
Background and Objective: Colorectal cancer is a high mortality cancer.
Clinical data analysis plays a crucial role in predicting the survival of
colorectal cancer patients, enabling clinicians to make informed treatment
decisions. However, utilizing clinical data can be challenging, especially when
dealing with imbalanced outcomes. This paper focuses on developing algorithms
to predict 1-, 3-, and 5-year survival of colorectal cancer patients using
clinical datasets, with particular emphasis on the highly imbalanced 1-year
survival prediction task. To address this issue, we propose a method that
creates a pipeline of some of standard balancing techniques to increase the
true positive rate. Evaluation is conducted on a colorectal cancer dataset from
the SEER database. Methods: The pre-processing step consists of removing
records with missing values and merging categories. The minority class of
1-year and 3-year survival tasks consists of 10% and 20% of the data,
respectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN),
Synthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and
RENN approaches were used and compared for balancing the data with tree-based
classifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient
Boosting, and Light Gradient Boosting (LGBM) are used in this article. Method.
Results: The performance evaluation utilizes a 5-fold cross-validation
approach. In the case of highly imbalanced datasets (1-year), our proposed
method with LGBM outperforms other sampling methods with the sensitivity of
72.30%. For the task of imbalance (3-year survival), the combination of RENN
and LGBM achieves a sensitivity of 80.81%, indicating that our proposed method
works best for highly imbalanced datasets. Conclusions: Our proposed method
significantly improves mortality prediction for the minority class of
colorectal cancer patients.