A Data Analytics Approach for Revealing Influencing Factors of HPV-Related Cancers From Population-Level Statistics Data - Du, Xiaoqin; Tan, Qi (Frontiers in Physics, 2021)
Human Papillomavirus (HPV) is considered as one of the major causes of multiple cancers, including cervical, anal, and vaginal cancers. Some studies analyzed the infection patterns of HPV-causing cancers using individual clinical test data, which is resource and time expensive. In order to facilitate the understanding of HPV-causing cancers, we propose to use data analytics methods to reveal the influencing factors from the population-level statistics data, which is available more easily. Particularly, we demonstrate the effectiveness of data analytics approach by introducing a predictive analytics method in studying the risk factors of cervix cancer in US. Besides accurate prediction of the number of infections, the predictive analytics method discovers the population statistic factors that most affect the cervical cancer infection pattern. Furthermore, we discuss the potential directions in developing more advanced data analytics approaches in studying HPV-causing cancers.