Ensemble Learning for Multi-Class Classification in Cervical Cancer Risk Factor Analysis: An Empirical Computational Approach

One cancer that affects women of all ages frequently and is also particularly fatal is cervical cancer. It is fatal because it cannot be anticipated in the early stages when the patient could be treated properly. When cervical cancer cells begin to migrate to nearby organs including the liver, lymph nodes, and lungs, they block the tube that takes urine from the bladder and cause symptoms. Leg discomfort, exhaustion, weight loss, vaginal bleeding, bone fractures, and pelvic pain are among the symptoms in this latter stage. Human papillomavirus (HPV), smoking, numerous pregnancies, using hormonal contraception, and other risk factors include cervical cancer. Screening is a crucial step in the detection of cervical cancer. This study suggests a technique for categorizing cervical cancer risk variables into four separate classes using a multi-class approach. The ensemble learning approach aims to increase the suggested model's accuracy. This technique aids in enhancing the study of risk factors used to determine the likelihood of developing cervical cancer. A multi-layer feed-forward network is used to examine the suggested Artificial Bee colony (ABC) along with a deep learning method known as the LST Model for various screening methods. The gradient boosting method with Multi-Layer Perceptron (MLP) is found to produce a higher % for all performance measures using the suggested model.