Efficient heart disease prediction system using decision tree

Description

ABSTRACT:
Cancer is one of the major problem today, diagnosing cancer in earlier stage is still challenging for doctors. Identification of genetic and environmental factors is very important in developing novel methods to detect and prevent cancer. Therefore a novel multi layered method combining clustering and decision tree technique is used to build a cancer risk prediction system. The proposed system is predicts lung, breast, oral, cervix, stomach and blood cancers and it is user friendly and cost saving. This research uses data mining techniques such as classification, clustering and prediction to identify potential cancer patients. We have proposed this cancer prediction system based on data mining techniques. This system estimates the risk of the breast cancer in the earlier stage. This system is validated by comparing its predicted results with patient’s prior medical information. The main aim of this model is to provide the earlier warning to the users and it is also cost efficient to the user. Finally a prediction system is developed to analyze risk levels which help in prognosis. This research helps in detection of a person’s predisposition for cancer before going for clinical and lab tests which is cost and time consuming.