Min Liu, Shiyu Liu, Zhaolin Lu, Hu Chen, Yuling Xu, Xue Gong, Guangxia Chen
{"title":"基于机器学习的成人幽门螺旋杆菌感染预测研究。","authors":"Min Liu, Shiyu Liu, Zhaolin Lu, Hu Chen, Yuling Xu, Xue Gong, Guangxia Chen","doi":"10.12659/MSM.943666","DOIUrl":null,"url":null,"abstract":"<p><p>BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has been widely used in the field of medical research and has become a hotspot in recent years. This paper proposed a prediction model for H. pylori infection based on machine learning in adults. MATERIAL AND METHODS Adult patients were selected as research participants, and information on 30 factors was collected. The chi-square test, mutual information, ReliefF, and information gain were used to screen the feature factors and establish 2 subsets. We constructed an H. pylori infection prediction model based on XGBoost and optimized the model using a grid search by analyzing the correlation between features. The performance of the model was assessed by comparing its accuracy, recall, precision, F1 score, and AUC with those of 4 other classical machine learning methods. RESULTS The model performed better on the part B subset than on the part A subset. Compared with the other 4 machine learning methods, the model had the highest accuracy, recall, F1 score, and AUC. SHAP was used to evaluate the importance of features in the model. It was found that H. pylori infection of family members, living in rural areas, poor washing hands before meals and after using the toilet were risk factors for H. pylori infection. CONCLUSIONS The model proposed in this paper is superior to other models in predicting H. pylori infection and can provide a scientific basis for identifying the population susceptible to H. pylori and preventing H. pylori infection.</p>","PeriodicalId":48888,"journal":{"name":"Medical Science Monitor","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168235/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults.\",\"authors\":\"Min Liu, Shiyu Liu, Zhaolin Lu, Hu Chen, Yuling Xu, Xue Gong, Guangxia Chen\",\"doi\":\"10.12659/MSM.943666\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has been widely used in the field of medical research and has become a hotspot in recent years. This paper proposed a prediction model for H. pylori infection based on machine learning in adults. MATERIAL AND METHODS Adult patients were selected as research participants, and information on 30 factors was collected. The chi-square test, mutual information, ReliefF, and information gain were used to screen the feature factors and establish 2 subsets. We constructed an H. pylori infection prediction model based on XGBoost and optimized the model using a grid search by analyzing the correlation between features. The performance of the model was assessed by comparing its accuracy, recall, precision, F1 score, and AUC with those of 4 other classical machine learning methods. RESULTS The model performed better on the part B subset than on the part A subset. Compared with the other 4 machine learning methods, the model had the highest accuracy, recall, F1 score, and AUC. SHAP was used to evaluate the importance of features in the model. It was found that H. pylori infection of family members, living in rural areas, poor washing hands before meals and after using the toilet were risk factors for H. pylori infection. CONCLUSIONS The model proposed in this paper is superior to other models in predicting H. pylori infection and can provide a scientific basis for identifying the population susceptible to H. pylori and preventing H. pylori infection.</p>\",\"PeriodicalId\":48888,\"journal\":{\"name\":\"Medical Science Monitor\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168235/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Science Monitor\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.12659/MSM.943666\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Science Monitor","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12659/MSM.943666","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults.
BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has been widely used in the field of medical research and has become a hotspot in recent years. This paper proposed a prediction model for H. pylori infection based on machine learning in adults. MATERIAL AND METHODS Adult patients were selected as research participants, and information on 30 factors was collected. The chi-square test, mutual information, ReliefF, and information gain were used to screen the feature factors and establish 2 subsets. We constructed an H. pylori infection prediction model based on XGBoost and optimized the model using a grid search by analyzing the correlation between features. The performance of the model was assessed by comparing its accuracy, recall, precision, F1 score, and AUC with those of 4 other classical machine learning methods. RESULTS The model performed better on the part B subset than on the part A subset. Compared with the other 4 machine learning methods, the model had the highest accuracy, recall, F1 score, and AUC. SHAP was used to evaluate the importance of features in the model. It was found that H. pylori infection of family members, living in rural areas, poor washing hands before meals and after using the toilet were risk factors for H. pylori infection. CONCLUSIONS The model proposed in this paper is superior to other models in predicting H. pylori infection and can provide a scientific basis for identifying the population susceptible to H. pylori and preventing H. pylori infection.
期刊介绍:
Medical Science Monitor (MSM) established in 1995 is an international, peer-reviewed scientific journal which publishes original articles in Clinical Medicine and related disciplines such as Epidemiology and Population Studies, Product Investigations, Development of Laboratory Techniques :: Diagnostics and Medical Technology which enable presentation of research or review works in overlapping areas of medicine and technology such us (but not limited to): medical diagnostics, medical imaging systems, computer simulation of health and disease processes, new medical devices, etc. Reviews and Special Reports - papers may be accepted on the basis that they provide a systematic, critical and up-to-date overview of literature pertaining to research or clinical topics. Meta-analyses are considered as reviews. A special attention will be paid to a teaching value of a review paper.
Medical Science Monitor is internationally indexed in Thomson-Reuters Web of Science, Journals Citation Report (JCR), Science Citation Index Expanded (SCI), Index Medicus MEDLINE, PubMed, PMC, EMBASE/Excerpta Medica, Chemical Abstracts CAS and Index Copernicus.