Machine learning and Cox proportional hazards regression model for warning of persistent infection with high-risk HPV type

DING Hongmei; ZHANG Mingya; XU Xiaoqin; ZHANG Hongxiu

doi:10.16462/j.cnki.zhjbkz.2024.09.014

Volume 28 Issue 9

Sep. 2024

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DING Hongmei, ZHANG Mingya, XU Xiaoqin, ZHANG Hongxiu. Machine learning and Cox proportional hazards regression model for warning of persistent infection with high-risk HPV type[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1083-1089. doi: 10.16462/j.cnki.zhjbkz.2024.09.014

Citation:

DING Hongmei, ZHANG Mingya, XU Xiaoqin, ZHANG Hongxiu. Machine learning and Cox proportional hazards regression model for warning of persistent infection with high-risk HPV type[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1083-1089. doi: 10.16462/j.cnki.zhjbkz.2024.09.014

Citation:

DING Hongmei, ZHANG Mingya, XU Xiaoqin, ZHANG Hongxiu. Machine learning and Cox proportional hazards regression model for warning of persistent infection with high-risk HPV type[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1083-1089. doi: 10.16462/j.cnki.zhjbkz.2024.09.014

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Machine learning and Cox proportional hazards regression model for warning of persistent infection with high-risk HPV type

doi: 10.16462/j.cnki.zhjbkz.2024.09.014

1.
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
2.
State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing 210023, China
3.
Fenghuang Community Health Service Center, Gulou District, Nanjing, Nanjing 210024, China
4.
Department of Obstetrics and Gynecology, The Fourth People′s Hospital of Taizhou, Taizhou 225300, China

Funds:

Scientific Research Project of Jiangsu Maternal and Child Health Care Association FYX202345

More Information

Corresponding author: ZHANG Hongxiu, E-mail: hongxiuz@njmu.edu.cn
Received Date: 2023-08-31
Rev Recd Date: 2024-03-16

Available Online: 2024-10-24

Publish Date: 2024-09-10

Abstract

Abstract

Objective A prediction model of human papillomavirus based on machine learning was established to determine the factors associated with the persistent infection of high-risk human papilloma virus(HR-HPV), so as to provide early warning for the persistent infection of HR-HPV. Methods Clinical data of 4 407 women who participated in HPV testing at four health centers in Taizhou City from September 2017 to September 2019 and participated in HPV follow-up from September 2020 to September 2022 were collected. The demographic characteristics of total 4 407 subjects in this cohort study were used as the input of the machine learning model, and the change process of the results of the two HPV inspections as the output, a prediction model based on machine learning was established, including random forest and multi-layer perceptron, to predict the HPV follow-up results of the research object. Univariate Cox risk proportion regression model and multivariate Cox risk proportion regression model were used to statistically analyze 583 primary screening HR-HPV positive cases. Results The accuracy of the random forest prediction model was 84.3%, and the accuracy of the multi-layer perceptron was 80.5%. The top five viral types with persistent positive rate of HR-HPV were HPV58, multiple infections, HPV31, HPV33, and HPV52. The multivariate Cox regression analysis showed that the conversion risk of HR-HPV infection in those with junior high school education or below was 1.72 times that of those with high school education and above (HR=1.72, 95% CI: 1.03-2.87, P=0.037), and the conversion risk of HR-HPV infection in non-menopausal individuals was 2.11 times higher than that in menopausal individuals (HR=2.11, 95% CI: 1.10-4.06, P=0.025). Conclusions Machine learning and Cox regression analysis models can provide an early warning of the HR-HPV persistent infection population, which has an important clinical value for the subsequent management of HR-HPV-infected women and the prevention and control of cervical cancer.
- Machine learning,
- Cox proportional hazards regression model,
- High-risk human papillomavirus,
- Persistent infection,
- Cervical cancer

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References(17)

References

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