Imbalanced survival prediction for gastric cancer patients based on improved XGBoost with cost sensitive and focal loss

Abstract

Accurate prediction of gastric cancer survival state is one of great significant tasks for clinical decision‐making. Many advanced machine learning classification techniques have been applied to predict the survival status of cancer patients in three or 5 years, however, many of them have a low sensitivity because of class imbalance. This is a non‐negligible problem due to the poor prognosis of gastric cancer patients. Furthermore, models in the medical domain require strong interpretability to increase their applicability. Due to the better performance and interpretability of the XGBoost model, we design a loss function taking into account cost sensitive and focal loss from the algorithm level for XGBoost to deal with the imbalance problem. We apply the improved model into the prediction of the survival status of gastric cancer patients and analyse the important related features. We use two types of indicators to evaluate the model, and we also design the confusion matrix of two models' predictive results to compare two models. The results show that the improved model has better performance. Furthermore, we calculate the importance of features related to survival with three different time periods and analyse their evolution, which are consistent with existing clinical research or further expand their research conclusions. These all support for clinically relevant decision‐making and has the potential to expand into survival prediction of other cancer patients.