Kawasaki disease (KD) is a disease that cause inflammation in blood vessels, impacting kids and causes heart and blood vessel damage. Patients with KD who have medium or big aneurysms, developing coronary dilatation for a minimum of 2 months, are related to the worst late coronary results, which are not widely researched. The primary goal of the suggested technique is to introduce an intelligent framework that can perform clinical data mining more accurately. In this, clinical data mining is done by using the novel hybridized classification approach. Initially, to replace the missing data, the Random Forest (RF) algorithm is proposed. Then, using the optimization algorithm, the feature selection is done, namely the artificial bee colony approach, which would select the best feature from the training data set. F-score values are considered as the fitness values for optimal feature selection. Finally, the classification process is done using the proposed hybridization approach; clustering is done before classification and in every iteration of classification. The clustering is done using the FCM clustering, and Deep neural network is employed for classification. The simulation results identified that the proposed hybridization approach has better classification accuracy, leading to an efficient assessment of KD.
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