title,doi,url,abstract,journal,publication_year,pmid,arxiv
Lung Cancer Predict- Risk Survivability for Radon Gas Among Non-Smokers Using Image Mining Techniques, https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT1726315, Today the world has shrunk using various technology .But at the same time some diseases like cancer is very risk for non-smokers. We use some image data is the one of the essential features in the present scenario since image mining data plays vital role of every aspect the system such as business for marketing hospital construction on particular data. Lung cancer is one of the leading cancer for menwomenkids developed including in India. Today the diseases are different. Some diseases causes very fast with our environment. We don’t know the name of the diseases also. Here we were approaching most of the diseases are caused by gases. Especially Radon (Rn). It is actually natural gas and also chemically inert. The main factor of cancer diseases are caused by various types like Bladder cancer Lung cancer(1) Brain cancer. Breast cancer Cervical cancer Ovarian cancer Throat cancer stomach cancer etc.Most of the people were affected lung cancer whether smokers or non-smokers. That is why we proposed using image mining approaches and identifying how cure it .Image mining is the mixture of data mining and image processing. It is the knowledge based extraction of data. This research technique is used q-value method about the gas content and how it is affected non-smokers only. Because smokers are smokes already gases. So we have a factors like to non-smokers such as building workers office workers kids women.etc.Here q-value is  denoted quality and  quantity approaches method are used three parameters such as bloodtissuessize.That is  q-statistics value of  Radon  affected by non-smokers and possibilities of curing diseases.Lung cancer Seems to be the common cancer cause of death among people It focus on prediction and detection as early as in the mining data representation for curing possibilities among non-smokers. , International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2017, CSEIT1726315