Weighted Clustering for Deep Learning Approach in Heart Disease Diagnosis

Abstract

An approach for heart diagnosis based on weighted clustering is presented in this paper. The existing heart diagnosis approach develops a decision based on correlation of feature vector of a querying sample with available knowledge to the system. With increase in the learning data to the system the search overhead increases. This tends to delay in decision making. The linear mapping is improved by the clustering process of large database information. However, the issue of data clustering is observed to be limited with increase in training information and characteristic of learning feature. To overcome the issue of accurate clustering, a weighted clustering approach based on gain factor is proposed. This approach updates the cluster information based on dual factor monitoring of distance and gain parameter. The presented approach illustrates an improvement in the mining performance in terms of accuracy, sensitivity and recall rate.