A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy Integrated Approach Two Level

Cardiovascular disease remains the biggest cause of deaths worldwide and the Heart Disease Prediction at the early stage is importance. As large amount of data is generated in medical organizations (hospitals, medical centers) but as this data is not properly used. There is a wealth of hidden information present in the datasets. This unused data can be converted into useful data. For this purpose we can use different data mining techniques. In this paper, we have defined a two layered approach for identifying the disease possibility. The critical factors that are mandatory for occurrence of coronary heart disease are taken at first level and the rest one are taken at second level. This two level approach increases the performance of our work as it helps in predicting disease chances accurately. The heart disease dataset is taken from UCI machine learning repository to train the neural network and then fuzzy rules are applied to predict the chances of coronary heart disease as low, medium or critical.