Special Issue Article
Effective Heart Disease Prediction using Frequent Feature Selection Method
The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. The diagnosis of heart disease is a significant and tedious task in medicine. This research paper proposed a frequent feature selection method for Heart Disease Prediction. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The none additively of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. Clustering the objects which have similar meaning, the proposed approach improves the accuracy and reduces the computational time.