Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach

Objectives To develop a Web-based tool to draw attention to patients positive for human papillomavirus (HPV) who have a high risk of progression to cervical cancer, in order to increase compliance with follow-up examinations and facilitate good doctor-patient communication. Methods Records were retrospectively analysed from women who were positive for HPV on initial testing (before any treatment). Information concerning age, Papanicolaou (PAP) smear result and presence of 15 high-risk HPV genotypes was used in a support vector machine (SVM) model, to identify the patient features that maximally contributed to progression to high-risk cervical lesions. Results Data from 731 subjects were analysed. The maximum number of correct cancer predictions was seen when four features (PAP, HPV16, HPV52 and HPV35) were used, giving an accuracy of 74.41%. A web-based high-risk cervical lesion prediction application tool was developed using the SVM model results. Conclusions Use of the web-based prediction tool may help to increase patient compliance with physician advice, and may heighten awareness of the significance of regular follow-up HPV examinations for the prevention of cervical cancer, in Korean women predicted to have heightened risk of the disease.