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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

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A 54-year-old man, a non-smoker, suffering from metastatic lung adenocarcinoma presented with extensive bilateral pulmonary infiltrates. He was dyspneic at rest. Performance status (PS) was 4. Institution of gefitinib resulted in relief from dyspnea within two weeks. Positron emission tomography done after 10 months revealed only a 2 cm residual lesion. However, the patient stopped therapy on his own and died two months later. An 80-year-old female, a non smoker, presented with metastatic lung adenocarcinoma and right sided pleural effusion. Her PS was 4. She was started on gefitinib. Within four weeks, she showed marked improvement. At six months, she was radiologically documented to be in partial remission. She continues to be asymptomatic at one year follow-up. These are the first reports of dramatic responses to gefitinib when used as front-line therapy in patients with poor performance status from India.