A Robust Boosting Model for detecting Cervical Cancer Using Histogram Boosting Gradient Classifier
N. Meenakshisundaram, G. Ramkumar
Abstract
When it comes to cancers that strike women, cervical cancer is in the top five worldwide. Human papillomavirus (HPV) infection is linked to the development of cervical cancer. The worldwide burden of cervical cancer has been reduced thanks to early screening, which has turned the illness into a preventive one. Due to factors such as the high cost of frequent examination, low levels of knowledge, and a lack of access to the medical facility, women in poor countries do not participate in screening programs in adequate numbers. To put it another way, this raises the bar for what may be considered a safe level of risk for any given patient. The development of cervical cancer has several risk factors. In order to assess the probability of cervical cancer developing, this research suggests a method called HBGC that use Histogram Boosting Gradient algorithms. Finally, the best forecasting algorithm was identified by contrasting GBC with the currently available approaches.