Prediction of Cervical Cancer Using Chicken Swarm Optimization

Abstract

Chicken Swarm Optimization algorithm for feature selection is proposed in this paper, which can be used for the prediction of cervical cancer. Cervical Cancer is the type of cancer that occurs at the cells of the cervix—the lower part of the uterus—which connects the vagina. This kind of cancer is generally caused by several strains of the human papillomavirus (HPV), a sexually transmitted infection. Feature Selection is a tool of optimization algorithm and plays an active role in the area of machine learning. The amount of data available for processing in machine learning problems has increased rapidly in recent years. So, the feature selection was introduced to solve this problem. Feature Selection is used when there is a need to eliminate such redundant features so that a better subset of features can be obtained by which dimensionality of dataset is reduced considerably. The Chicken Swarm Optimization is an algorithm method inspired by nature, which is used for optimization techniques, proposed for feature selection for prediction of cervical cancer. Impersonating the hierarchical order in the Chicken Swarm, which includes hens, roosters, and chicks. CSO can productively extricate the chickens’ swarm intelligence to optimize problems. CSO has the ability to attain exceptional optimization results in terms of optimization correctness. In CSO, the chicken swarm is divided into various sets or groups, which consist of a single rooster and a number of hens and chicks. Different chickens follow various kinds of motion. There exists competition among various chickens under specific hierarchical order. We hope that with the help of this project, cervical cancer can be predicted early and proper treatment can be provided on time. The proposed Chicken Swarm Optimization shows the best accuracy in the feature selection from the Cervical Cancer dataset with a very fast computational time of a few seconds.