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Comparative Research on Predicting Precancerous Gastric Conditions Using Machine Learning
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Yikang SHEN1, Haiyu LIU1, Shaoli WANG2, Xiaoping ZHANG1, Xiaonan HU1, Yifang LOU1, Zhen LIU2, Xiaoxia XIE1, Yufeng ZHAO1
Chinese Archives of Traditional Chinese Medicine | 2025, 43(6) : 31 - 35
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(6): 31-35
• Digital Traditional Chinese Medicine •
Comparative Research on Predicting Precancerous Gastric Conditions Using Machine Learning
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Yikang SHEN1, Haiyu LIU1, Shaoli WANG2, Xiaoping ZHANG1, Xiaonan HU1, Yifang LOU1, Zhen LIU2, Xiaoxia XIE1, Yufeng ZHAO1
Affiliations
  • 1.Data Center of Traditional Chinese Medicine，China Academy of Chinese Medical Sciences，Beijing 100700，China
  • 2.Guang'anmen Hospital，China Academy of Chinese Medical Sciences，Beijing 100700，China
Published: 2025-06-10 doi: 10.13193/j.issn.1673-7717.2025.06.005
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Gastric cancer is one of the most common malignant tumors in the digestive tract and poses a serious threat to human health.Early warning and diagnosis can increase the 5-year survival rate by more than 60%.In recent years，machine learning methods have achieved rich results in predicting the precancerous state of gastric cancer.This article first discussed the significant features of the precancerous state of gastric cancer from the aspects of endoscopic images，biochemical markers，gene expression，and traditional Chinese medicine syndromes，and analyzed the advantages and disadvantages of traditional machine learning methods，deep learning methods and various fusion methods by combining the research results of the precancerous state of gastric cancer predicted by machine learning methods.It proposed three suggestions for carrying out multi-center，large-sample cohort studies and building shared data platforms，realizing multi-modal data fusion and enhancing the interpretability of the model，providing methodological references for the“early diagnosis and treatment”of gastric cancer.

gastric cancer  /  early diagnosis and treatment  /  disease state prediction  /  machine learning
沈翊康, 刘海煜, 王少丽, 张小平, 胡晓楠, 楼一方, 刘震, 谢晓霞, 赵玉凤. 基于机器学习方法预测胃癌前状态的比较研究. 中华中医药学刊, 2025 , 43 (6) : 31 -35 . DOI: 10.13193/j.issn.1673-7717.2025.06.005
Yikang SHEN, Haiyu LIU, Shaoli WANG, Xiaoping ZHANG, Xiaonan HU, Yifang LOU, Zhen LIU, Xiaoxia XIE, Yufeng ZHAO. Comparative Research on Predicting Precancerous Gastric Conditions Using Machine Learning[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (6) : 31 -35 . DOI: 10.13193/j.issn.1673-7717.2025.06.005
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Year 2025 volume 43 Issue 6
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doi: 10.13193/j.issn.1673-7717.2025.06.005
  • Online Date：2026-10-30
  • Published：2025-06-10
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    1.Data Center of Traditional Chinese Medicine，China Academy of Chinese Medical Sciences，Beijing 100700，China
    2.Guang'anmen Hospital，China Academy of Chinese Medical Sciences，Beijing 100700，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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