Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Abstract

Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients' survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem. The first is to predict the patients' five-year survivability. If the patient's predicted outcome is death, the second stage predicts the remaining lifespan of the patient. Our research proposes a custom ensemble method which integrated multiple machine learning algorithms. It exhibits a significant predictive improvement in both stages of prediction, compared with the state-of-the-art machine learning techniques. The base machine learning techniques include Decision Trees, Random Forest, Adaboost, Gradient Boost Machine (GBM), Artificial Neural Network (ANN), and the most popular GBM framework-LightGBM. The model is comprehensively evaluated on open source cancer data provided by the Surveillance, Epidemiology, and End Results Program (SEER) in terms of accuracy, area under the curve, Fscore, precision, recall rate, training and predicting time in the classification stage, and root mean squared error, mean absolute error, coefficient of determination (R 2) in the regression stage.

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

“…In addition, a large sample of data is required to obtain meaningful results for multivariate techniques. 8,20 In the past, researchers have used a variety of survival analysis methods to describe the relationship between response variables and a set of independent variables in various fields of medical science. In this context, conventional survival methods such as Cox proportional hazard modeling are still the most common approach for analyzing the relative importance of the predictive variables in the development of the disease.…”

“…Many clinical predictors influence gastric cancer. In the reviewed studies, after doing feature ranking, the variables such as age, 6,35,48,49 gender, 36,47,50 body mass index, 6,47,50 Karnofsky performance scale, 8,48,51 TNM stage, 36,[47][48][49][50] tumor grade, 7,8,35,[47][48][49][50] tumor size, 6,7,47,[49][50][51] tumor location, 6,7,35,36,48,49 lymphovascular invasion, 7,8,47,49,50 active and timely treatment, 7,8,36 type of treatment, 35,49 disease stage and severity, 6,8,…”

“…6,7 The 5-year relative survival rate is up to 70% for lesions in the early stages and 4% for lesions in the advanced stages. 6,8,9 Survival often refers to the likelihood, by which a patient will live 60 months after being diagnosed with cancer. This index is commonly used in medical science to evaluate the effects of surgical and treatment plans.…”