Optimization Enabled Black Hole Entropic Fuzzy Clustering Approach for Medical Data

Abstract

Abstract Medical data clustering is an important part of medical decision systems as it refines highly sensitive information from the huge medical datasets. Medical data clustering includes processes, like determine random clusters, set data into specified clusters and handle data clusters dynamically. Hence, handling of medical data streams and clustering remains a challenging issue. This paper proposes a technique, namely Rider-based sunflower optimization (RSFO) for medical data clustering. Initially, the significant features are selected using the Tversky index with holoentropy that is established from the input data. The holo-entropy is utilized to analyze the relationship between the attributes and features. Here, the clustering is done by a Black Hole Entropic Fuzzy Clustering (BHEFC) algorithm, where the optimal cluster centroids are selected by the proposed RSFO algorithm. The proposed RSFO is designed by incorporating the Rider optimization algorithm (ROA) and sunflower optimization (SFO). The effectiveness of the proposed BHEFC+RSFO algorithm is analyzed by the Dermatology Data Set, and the proposed method has the maximal accuracy of 94.480%, Jaccard coefficient of 94.224% and Rand coefficient of 91.307%, respectively.