Informatics Publishing Limited

T. Sowmiya ¹, M. Gopi ¹, M. New Begin ¹, L. Thomas Robinson ¹

Affiliations
1 Department of Master of Computer Applications, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, IN

Abstract

Now a day the most dangerous diseases in the world are Cancer. Lung cancer is one of the most dangerous cancer types in the world. These diseases can spread worldwide by uncontrolled cell growth in the tissues of the lung. Early detection of the cancer can save the life and survivability of the patients who affected by this diseases. In this paper we survey several aspects of data mining procedures which are used for lung cancer prediction for the patients. Data mining concepts is useful in lung cancer classification. We also reviewed the aspects of ant colony optimization (ACO) technique in data mining. Ant colony optimization helps in increasing or decreasing the disease prediction value of the diseases. This case study assorted data mining and ant colony optimization techniques for appropriate rule generation and classifications on diseases, which pilot to exact Lung cancer classifications. In additionally to, it provides basic framework for further improvement in medical diagnosis on lung cancer. Our Proposed idea for the lung cancer optimization on data mining is by using the (ROCO) method. We use reduced-order constrained optimization (ROCO) to create clinically acceptable IMRT plans quickly and automatically for advanced lung cancer patients Diagnosis. Our new ROCO implementations works with the treatment planning system and full dose calculations used at Memorial Sloan-Kettering Cancer Center for diagnosis, and we have implemented the mean dose hard-constraints on cancer, along with the point-dose and dosage-volume constraints that we used for our previous work on the prostate.

Keywords

ACO, Data Mining, Rule Pruning, ROCO.

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