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. While other state of art methods like decision tree or naïve bays classifiers consider that feature is independent of each other. 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 (associative classifier) that uses weighted association rule mining (WARM). In any prediction model all attributes do not have same importance in predicting the class label. So different weights can be assigned to different attributes according to their predicting capability. Experiments have been performed using three Medical data set  (UCI Machine learning dataset) and the results reveal that by assigning weight to the attributes the prediction accuracy improves. The average accuracy is better as compare to other associative classifiers i.e. CBA, CMAR and CPAR. The result reveals that WAC is a promising alternative in medical prediction and certainly deserves further attention.