NDLI: Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology

Content Provider	Springer Nature : BioMed Central
Author	Zhou, Cheng-Mao Wang, Ying Yang, Jian-Jun Zhu, Yu
Abstract	Objective There is a strong association between gastric cancer and inflammatory factors. Many studies have shown that machine learning can predict cancer patients’ prognosis. However, there has been no study on predicting gastric cancer death based on machine learning using related inflammatory factor variables. Methods Six machine learning algorithms are applied to predict total gastric cancer death after surgery. Results The Gradient Boosting Machine (GBM) algorithm factors accounting for the prognosis weight outcome show that the three most important factors are neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) and age. The total postoperative death model showed that among patients with gastric cancer from the predictive test group: The highest accuracy was LR (0.759), followed by the GBM algorithm (0.733). For the six algorithms, the AUC values, from high to low, were LR, GBM, GBDT, forest, Tr and Xgbc. Among the six algorithms, Logistic had the highest precision (precision = 0.736), followed by the GBM algorithm (precision = 0.660). Among the six algorithms, GBM had the highest recall rate (recall = 0.667). Conclusion Postoperative mortality from gastric cancer can be predicted based on machine learning.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-023-02150-2.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-023-02150-2
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-03-31
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Machine learning Gastric cancer Postoperative Death Inflammatory
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

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