Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining

Abstract

Associative classifiers are new classification approach that use association rules for classification. An important advantage of these classification systems is that, using association rule mining (ARM) they are able to examine several features at a time. Many applications can benefit from good classification model. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Medical diagnosis is a domain where the maximum accuracy of the model is desired. In this paper, we propose a framework weighted associative classifier (WAC) that assigns different weights to different attributes according to their predicting capability. We are using maximum likelihood estimation (MLE) theory to calculate weight of each attribute using training data. We also show how existing Apriori algorithm can be modified in weighted environment to infer association rule from medical dataset having numeric valued attributes as the conventional ARM usually deals with the transaction database with categorical values. Experiments have been performed on benchmark data set to evaluate the performance of WAC in terms of accuracy, number of rules generating and impact of minimum support threshold on WAC outcomes. The result reveals that WAC is a promising alternative in medical prediction and certainly deserves further attention.