Novel clustering of bigger and complex medical data by enhanced fuzzy logic structure

The significant contribution of the clustering algorithm for diagnosis of the clinical condition through medical data consideration is must in the healthcare sector. The currently existing techniques implement the Fuzzy Logic in clustering and have been found by research gap which describes that less focus on the medical data clustering. Thus, this paper introduced a novel algorithm where the enhancement of fuzzy logic is performed to achieve better computational ability in the processing of highly complex medical data such as microarray data. The introduced algorithm is implemented for disease diagnosis and classification. The outcomes of the proposed algorithm are compared with recent approaches like the genetic algorithm, support vector machine (SVM), and artificial neural network (ANN). On analyzing these comparative results found that the proposed clustering model achieved significant performance in response time and classification of disease with better accuracy.