Case Based Reasoning (CBR) is a powerful problem-solving method that could be effectively used in medical science. But for the success of CBR, reasonable evaluation of case similarity is one of the key technologies. In order to solve feature weighting problem in case similarity a weighted nearest neighbour (w-NN) is considered based on inductive learning for swine flu prediction. Finally based on w-NN and extensibility in similarity, a novel case classification algorithm is proposed for swine

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... u prediction. The novel case classification algorithm exploits knowledge about hidden patterns and relationships from a huge collection of healthcare industrial data which is not properly mined and not put to the optimum use. Comparison of the proposed model is also done with an earlier proposed model and from the empirical results it is found that our model is very promising because it produces 85.6% accurate result that is superior to the earlier model.