This paper presents the Chicken Swarm Optimization algorithm for feature selection, 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 that connects to the vagina. Various strains of the human papillomavirus (HPV), a sexually transmitted infection, play a role in causing most cervical cancer. When exposed to HPV, a womans immune system typically prevents the virus from doing harm. In a small group of women, however, the virus survives for years, contributing to the process that causes some cells on the surface of the cervix to become cancer cells. Anyone can reduce the risk of cervical cancer by having screening tests and receiving a vaccine that protects against HPV infection. Feature Selection is a type of optimization algorithm and plays a vital role in the field of Machine Learning. In recent years there has been an exponential increase in the amount of data available for proce
ssing in Machine Learning problems. 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 which helps in reducing the dimensionality of a dataset. The Chicken Swarm Optimization Algorithm is a new bio-inspired optimization technique, which is proposed for feature selection for prediction of cervical cancer. Impersonating the hierarchical order in the chicken swarm, which includes roosters, hens, 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 accuracy. 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 amongst various chickens under specific hierarchical order.