文章基本信息

标题：Optimization of Lung Cancer using Modern Data Mining Techniques
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作者：T. Sowmiya ; M. Gopi ; M. New Begin 等
期刊名称：International Journal of Engineering Research
印刷版ISSN：2319-6890
出版年度：2014
卷号：3
期号：5
页码：309-314
出版社：IJER
摘要：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 classifica tions 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.
关键词：ACO; data mining; rule pruning; ROCO