Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model

Cheng Xu, Jing Wang, TianLong Zheng, Yue Cao, Fan Ye
2021 Archives of Medical Science  
IntroductionIt's very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification. We propose a new Random Forest weighting method and apply it to the gastric cancer patient data from the Surveillance, Epidemiology, and End Results (SEER) program, and then evaluated the generalization ability of this weighted Random Forest
more » ... hm on 10 public medical datasets. Furthermore, for the same weighting mode, the difference between using out-of-bag (OOB) data and all training sets as the weighting basis is explored.Material and methods110697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were contained in the experiment. In addition, 10 public medical datasets are used for the generalization ability evaluation of this weighted Random Forest algorithm.ResultsThrough experimental verification, on the SEER gastric cancer patient data, the weighted Random Forest algorithm improves the accuracy by 0.79% compared with the original Random Forest. In AUC, Macro-averaging increased by 2.32% and Micro-averaging increased by 0.51% on average. Among the 10 public datasets, the Random Forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1.44% in accuracy and an average increase of 1.2% in AUC.ConclusionsCompared with the original Random Forest, the weighted Random Forest model has a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data.
doi:10.5114/aoms/135594 pmid:36160349 pmcid:PMC9479734 fatcat:g5m7norxbvfd7fuwuvx3oqyvkm