Abstract:

The prediction of gastric cancer survivability is one of the important works in the gastric cancer prognosis， which can provide decision-making support for the doctor by mining important features affecting the survivability of gastric cancer patients and accurately predicting survivability. So far， the accuracy of survivability prediction of gastric cancer is insufficient， and the interpretability of prediction models is lack. Therefore， a gastric survivability prediction method called SGPL-LightGBM based on improved LightGBM is proposed to predict the survivability of patients with gastric cancer and interpret the prediction model. The stability-based feature selection method is carried out to determine the optimal feature subset， thereby reducing the computational overhead and improving the accuracy of the prediction. After that， the intelligent optimization algorithm （Genetic algorithm） is adopted to optimize the important hyperparameters in LightGBM （Light gradient boosting machine） to further improves the performance of gastric cancer survivability prediction model. The important features of gastric survivability prediction model are analyzed by PDP （Partial dependence plot） and LIME （Local interpretable model-agnostic explanations）， which explain the effect of influencing features on the predicted response of SGPL-LightGBM. Finally， the numeral experiments are conducted on real gastric dataset， and the experimental results indicate that the proposed ensemble classification prediction method SGPL-LightGBM has better accuracy and interpretability in gastric cancer survivability prediction， which can provide effective decision support for doctors to develop a treatment plan.

Key words: gastric cancer survival prediction, feature selection, ensemble learning, intelligence algorithm, interpretability