Comparative Analysis of Weighted Emphirical Optimization Algorithm and Lazy Classification Algorithms

Abstract

Health care has millions of centric data to discover the essential data is more important. In data mining the discovery of hidden information can be more innovative and useful for much necessity constraint in the field of forecasting, patient’s behavior, executive information system, e-governance the data mining tools and technique play a vital role. In Parkinson health care domain the hidden concept predicts the possibility of likelihood of the disease and also ensures the important feature attribute. The explicit patterns are converted to implicit by applying various algorithms i.e., association, clustering, classification to arrive at the full potential of the medical data. In this research work Parkinson dataset have been used with different classifiers to estimate the accuracy, sensitivity, specificity, kappa and roc characteristics.