Recognition and anticipation of cancer and non-cancer prophecy using data mining approach

Lung cancer is the number one cause of cancer deaths in both men and women in the worldwide. The two types of lung cancer, which grow and spread differently, are the small cell lung cancers (SCLC) and non-small cell lung cancers (NSCLC). Treatment of lung cancer can involve a combination of surgery, chemotherapy, and radiation therapy as well as newer experimental methods. The general prognosis of lung cancer is poor because doctors tend not to find the disease until it is at an advanced stage. Lung cancer in females will raise thirty-times faster than in males over the next thirty years shown in the survey. Early detection and anticipation of lung cancer the stage of important role in reducing deaths caused by cancer. We need to prevent the lung cancer. First we gather the data from hospitals, data centers and cancer research centers. The collected data is pre-processed and stored in the knowledge base to build the model. To Give Risk scores for the attributes that represent the significant patterns using Decision - Tree algorithm and the data is clustered using K-means clustering algorithm to separate cancer and non cancer patient data based on the risk score. If the patient contains cancer, Test the data and find the risk status using prediction.