Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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[Reivew]Prediction of Cervical Cancer Risk from Taking Hormone Contraceptivese

  • Su jeong RU (Dept. of BigData medical, Eulji University) ;
  • Kyung-A KIM (Dept. of Medical Artificial Intelligence, Eulji University) ;
  • Myung-Ae CHUNG (Dept. of BigData Medical Convergence, Eulji University) ;
  • Min Soo KANG (Dept. of Medical IT, Eulji University)
  • Received : 2023.01.19
  • Accepted : 2023.04.05
  • Published : 2024.03.30
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Abstract

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

Keywords

Acknowledgement

This work was Supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government in 2022. This work was supported by the research grant of the KODISA Scholarship Foundation in 2024.

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