Lung Cancer with Prediction Using Dbscan
Abstract
Data mining in healthcare is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Healthcare data mining attempts to solve real world health problems in the diagnosis and treatment of diseases. Researchers are using data mining techniques in the medical diagnosis of several diseases such as diabetes, stroke, cancer and heart disease. Lung cancer, also known as lung carcinoma, is a malignant lung tumor characterized by uncontrolled cell growth in tissues of the lung. This growth can spread beyond the lung by the process of metastasis into nearby tissue or other parts of the body. Most cancers that start in the lung, known as primary lung cancers, are carcinomas. The two main types are small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC). symptoms of lung cancer: thready pluse, wiry pulse, soft pulse, thin fur, yellow fur, white fur, obesity, red tongue, deep red tongue, pale tongue, internal bleeding, abdominal distension, stomachache, constipation, loose stool, cough, serious cough, occasional cough, phlegm, little phlegm, phlegm that is hard to cough up, dry mouth, chest pain, oppression in chest, sweating, fever, weakness, weak legs, rash, oral ulcer. As there is a big growth in large volume of data now days, this will create a need for extracting meaningful data from the information. From the various biomedical datasets, cancer is the widest disease that has killed human life over 7 million every year and lung cancer among them is nearly 17% of moralities. Previous research works show that survival rate of patients affected with cancer is larger and higher, when compared to the diagnosed at the initial stage, Lung cancer is the most historic data and dependent disease in for early diagnosis. This has created the researcher to use data mining technique for early diagnosis of lung cancer in stage 1.There has been an increase in survival rate to about 70% at the early stage of detection, when tumor is not spread. Pre- existing techniques The five year survival rate increases to 70% with the early detection at stage 1, when the tumor has not yet spread. Existing medical techniques like X-Ray, Computed Tomography (CT) scan, sputum cytology analysis and other imaging techniques not only require complex equipment and high cost but is also proven to be efficient only in stage 4, when the tumor has metastasized to other parts of the body. Our proposed work involves the uses of data mining technique used in classification of lung cancer patients and the categorization of stage to which it belong positive. The work is based on early diagnosis of prediction of lung cancer which suggests the doctors in treating the patients for increasing the survival rate of the human. For Early stage of prediction there are so many data mining techniques are there. I studied prediction using DBSCAN Algorithm
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