{"title":"A self-evaluated predictive model: A Bayesian neural network approach to colorectal cancer diagnosis","authors":"Jie Guo, Zihao Wu, Yin Jia, Hongwei Cao, Qin Qin, Tingting Sun, Xinru Zhou, Xue Pan, Cheng Hua, Chuanbin Mao, Shanrong Liu","doi":"10.1002/viw.20230050","DOIUrl":null,"url":null,"abstract":"Artificial intelligence has shown immense potential in cancer prediction, but existing models cannot estimate prediction uncertainty by themselves. Here, we developed a Bayesian neural network (BNN) model, BNN‐CRC15, for colorectal cancer (CRC) prediction while assessing its reliability. The model was trained on routine laboratory data obtained from 27,911 participants and provided quantified prediction uncertainty, allowing identification of a subset of participants in which the model was confident, mimicking the diagnostic process of human practitioners. Our model exhibited superior performance (area under the curve = 0.918) in the confident participant group, which accounted for 46.4% of the patients, indicating that routine laboratory data alone are sufficient for accurate predictions in this subset. For the non‐confident group, further advanced tests, such as colonoscopy, could be recommended to achieve more accurate predictions. In addition, our model demonstrated superior overall accuracy (0.848) in all patients, outperforming other five traditional algorithms (extreme gradient boosting, support vector machine, logistic regression, random forest, and artificial neural network) and fecal immunochemical test in distinguishing CRC from non‐CRC. These findings suggest that our BNN‐CRC15 model could serve as a valuable tool for improving CRC diagnosis and prevention.","PeriodicalId":34127,"journal":{"name":"VIEW","volume":"63 1","pages":""},"PeriodicalIF":9.7000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"VIEW","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/viw.20230050","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence has shown immense potential in cancer prediction, but existing models cannot estimate prediction uncertainty by themselves. Here, we developed a Bayesian neural network (BNN) model, BNN‐CRC15, for colorectal cancer (CRC) prediction while assessing its reliability. The model was trained on routine laboratory data obtained from 27,911 participants and provided quantified prediction uncertainty, allowing identification of a subset of participants in which the model was confident, mimicking the diagnostic process of human practitioners. Our model exhibited superior performance (area under the curve = 0.918) in the confident participant group, which accounted for 46.4% of the patients, indicating that routine laboratory data alone are sufficient for accurate predictions in this subset. For the non‐confident group, further advanced tests, such as colonoscopy, could be recommended to achieve more accurate predictions. In addition, our model demonstrated superior overall accuracy (0.848) in all patients, outperforming other five traditional algorithms (extreme gradient boosting, support vector machine, logistic regression, random forest, and artificial neural network) and fecal immunochemical test in distinguishing CRC from non‐CRC. These findings suggest that our BNN‐CRC15 model could serve as a valuable tool for improving CRC diagnosis and prevention.
期刊介绍:
View publishes scientific articles studying novel crucial contributions in the areas of Biomaterials and General Chemistry. View features original academic papers which go through peer review by experts in the given subject area.View encourages submissions from the research community where the priority will be on the originality and the practical impact of the reported research.