Knowledge Estimation with HPV and Cervical Cancer Risk Factors Using Logistic Regression

In as much as the study of biomedicine remain important to life, the application of statistical models for the purpose of analyzing and understanding the context of biomedical dataset and all other epidemiological research fields is as well vital. Relating and selecting the best statistical model for any biomedical dataset for proper and accurate interpretation of analytical result from complex dataset defines the basis of biomedical applications of regression models and modelling. The purpose of this paper is to model the relationship between two/more dependent variables acquired from Human Papillomavirus (HPV) dataset with the use Logistic Regression (LR) model. This model can differentiate between participants who have a low or high probability of adequate knowledge, attitude, and perception (KAP) about HPV-infections and cervical cancer disease. The HPV dataset was collected with the use of an online developed Human Papillomavirus (HPV) Assessment Test (HAT) tool that was made accessible to voluntary participants. The HAT tool comprises of datasets that are related to HPV and cervical cancer (CC).