Respiratory Cancerous Cells Detection Using TRISS Model and Association Rule Mining
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 3
Abstract
Lung cancer is a disease of uncontrolled cell growth in Epithelium of the lung, Lung cancer is one of the most common and deadly diseases in the world. Early and accurate detection of cancer is critical to the well being of patients. We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival Estradiol models for lung cancer using data mining techniques. The goal here is to identify characteristics of patient segments where average survival is significantly higher/ lower than average survival across the entire dataset. Several data mining classification techniques were used on the preprocessed data along with various data mining optimizations and validations. A subset of 13 patients attributes from the SEER data were recently linked with the survival outcome using Estradiol models, which is used in this study for segmentation. The resulting rules conform to existing biomedical knowledge and provide interesting insights into lung cancer surval
Authors and Affiliations
Ayyadurai. P , , Kiruthiga. P , Valarmathi. S , Amritha.
Keywords
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