Prediction of prognosis and survival of patients with gastric cancer by a weighted improved random forest model: an application of machine learning in medicine.
Introduction: It is essential 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...
| Publicado en: | Archives of Medical Science Vol. 18; no. 5; pp. 1208 - 1221 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Termedia Publishing House
2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159018949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159018949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17341922 77TB jtl: Archives of Medical Science issn: 17341922 maglogo: N pubinfo: dt: 2022 vid: 18 iid: 5 pid: 24295 pub: Termedia Publishing House place: , <Blank> artinfo: ui: 159018949 159018949 NLM36160349 10.5114/aoms/135594 NLM36160349 159018949 ppf: 1208 ppct: 13 formats: tig: atl: Prediction of prognosis and survival of patients with gastric cancer by a weighted improved random forest model: an application of machine learning in medicine. aug: au: Cheng Xu Jing Wang Tianlong Zheng Yue Cao Fan Ye Xu, Cheng Wang, Jing Zheng, Tianlong Cao, Yue Ye, Fan affil: College of Computer Science and Technology, Huaibei Normal University, Huaibei, China sug: ab: Introduction: It is essential 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. We evaluated the generalization ability of this weighted random forest algorithm 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 methods: 110 697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were included in the experiment. In addition, 10 public medical datasets were used for the generalization ability evaluation of this weighted random forest algorithm.Results: Through 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.Conclusions: Compared with the original random forest, the weighted random forest model shows a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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