A self-evaluated predictive model: A Bayesian neural network approach to colorectal cancer diagnosis

Jie Guo, Zihao Wu, Yin Jia, Hongwei Cao, Qin Qin, Tingting Sun, Xinru Zhou, Xue Pan, Cheng Hua, Chuanbin Mao, Shanrong Liu

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VIEW ›› 2024, Vol. 5 ›› Issue (1) : 20230050. DOI: 10.1002/VIW.20230050
RESEARCH ARTICLE

A self-evaluated predictive model: A Bayesian neural network approach to colorectal cancer diagnosis

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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.

Keywords

artificial intelligence / Bayesian neural network / colorectal cancer / routine laboratory tests / uncertainty quantification

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Jie Guo, Zihao Wu, Yin Jia, Hongwei Cao, Qin Qin, Tingting Sun, Xinru Zhou, Xue Pan, Cheng Hua, Chuanbin Mao, Shanrong Liu. A self-evaluated predictive model: A Bayesian neural network approach to colorectal cancer diagnosis. VIEW, 2024, 5(1): 20230050 https://doi.org/10.1002/VIW.20230050

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2023 2023 The Authors. View published by Shanghai Fuji Technology Consulting Co., Ltd, authorized by Professional Community of Experimental Medicine, National Association of Health Industry and Enterprise Management (PCEM) and John Wiley & Sons Australia, Ltd.
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