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Research Article
Jung Hyun Chang
National Health Insurance Service Ilsan Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data.
Methods
The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146). Our study cohort consisted of health screening examinees in 2004 or 2005 without cancer history, which was randomly split into training and test cohorts. Robust predictors were selected using Cox proportional hazard regression with 1,000 different bootstrapped datasets. Random forest and extreme gradient boosting algorithms were used to develop a prediction model for the 12-year risk of HCC development after screening. After optimizing a prediction model via cross validation in the training cohort, the model was validated in the test cohort.
Results
Of 331,694 examinees, 0.8% were diagnosed with HCC during the follow-up period (median, 11.2 years), respectively. Of the selected predictors, older age, male sex, abnormal liver function tests, the family history of chronic liver disease, and underlying chronic liver disease, chronic hepatitis virus or human immunodeficiency virus infection, and diabetes mellitus were associated with increased risk, whereas elevated total cholesterol and underlying dyslipidemia or schizophrenic/delusional disorders were associated with decreased risk of HCC development (p<0.001). In the test, our model showed good discrimination and calibration. The C-index, AUC, and Brier skill score were 0.868, 0.872, and 0.08, respectively.
Conclusions
Machine learning-based model could be used to predict the risk of HCC development based on the health screening examination results and claim data.
Keywords
Big Data, Machine Learning, Liver Neoplasms, Precision Medicine
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No competing interests reported.
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CURRENT STATUS: Under Revision
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Posted 26 Mar, 2021
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Editorial decision: Major revision
On 28 Apr, 2021
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Received 12 Apr, 2021
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On 03 Apr, 2021
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On 03 Apr, 2021
Reviewers agreed
On 03 Apr, 2021
Reviewers invited
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Editor invited
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This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jung Hyun Chang
National Health Insurance Service Ilsan Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 26 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 28 Apr, 2021
Reviews received
Received 12 Apr, 2021
Reviewers agreed
On 03 Apr, 2021
Reviewers agreed
On 03 Apr, 2021
Reviewers agreed
On 03 Apr, 2021
Reviewers invited
Invitations sent on 03 Apr, 2021
Editor assigned
On 29 Mar, 2021
Editor invited
On 26 Mar, 2021
Submission checks complete
On 25 Mar, 2021
First submitted
On 18 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data.
Methods
The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146). Our study cohort consisted of health screening examinees in 2004 or 2005 without cancer history, which was randomly split into training and test cohorts. Robust predictors were selected using Cox proportional hazard regression with 1,000 different bootstrapped datasets. Random forest and extreme gradient boosting algorithms were used to develop a prediction model for the 12-year risk of HCC development after screening. After optimizing a prediction model via cross validation in the training cohort, the model was validated in the test cohort.
Results
Of 331,694 examinees, 0.8% were diagnosed with HCC during the follow-up period (median, 11.2 years), respectively. Of the selected predictors, older age, male sex, abnormal liver function tests, the family history of chronic liver disease, and underlying chronic liver disease, chronic hepatitis virus or human immunodeficiency virus infection, and diabetes mellitus were associated with increased risk, whereas elevated total cholesterol and underlying dyslipidemia or schizophrenic/delusional disorders were associated with decreased risk of HCC development (p<0.001). In the test, our model showed good discrimination and calibration. The C-index, AUC, and Brier skill score were 0.868, 0.872, and 0.08, respectively.
Conclusions
Machine learning-based model could be used to predict the risk of HCC development based on the health screening examination results and claim data.
Figure 1
Figure 2
Figure 3
Figure 4
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