Prediction and Analysis of Cervical Cancer: An Ensemble Approach

Cancer is the one of the most fatal and uncurable diseases ever found in the medical history. Cervical cancer is one of those sub strains in the cancer. Cervical Cancer is found by the detection of HPV tumor cells in the female reproductory part. This is the fatal disease that can lead to death if left ignored without any further care to cure the tumor. This disease can be spread by various means such as sexual intercourse, and various reproductory methods for pleasure. Before any invention of machine learning techniques, various medical techniques and methods were used to detect the HPV cells in the female body such as Pap smear test, HCII hybrid, HPV typing and Southern blot hybridization assay test. This paper discusses various methods i.e., machine learning algorithms and ensemble classifier to detect and predict the cancer cells from the datasets recorded while inspecting the victim or patient cells in the hospital. These methods are implemented by building a model based on the training data based on the hospital records. The proposed ensemble-based model classifies various textual data of patients gathered from various hospitals to detect and predict the existence of cervical cancer with better prediction and accuracy.