Abstract
The Coronavirus disease 2019 (COVID-19) is considered as a pandemic by the World Health Organization (WHO), which has spread worldwide. Over millions of peoples are infected across the globe and several people are died. However, the most worrying group of patients suffered from lung severity with respiratory failure. Hence, cluster analysis is utilized for examining the heterogeneity of diseases for determining various clinical phenotypes having the same traits. This article devises an optimization-driven technique for COVID-19 patient analysis using the spark framework. Here, the input data is partitioned and fed to different slave nodes. In slave node, the selection of imperative features is done using the proposed poor and rich dolphin optimization algorithm (PRDOA). The proposed PRDOA is obtained by combining poor and rich (PRO) and dolphin echolation (DE) algorithm. The fitness is newly devised considering Minkowski distance measure. The clustering is performed on the master node using the proposed Tanimoto-based deep fuzzy clustering (TDFC) for effective COVID-19 patient analysis. Thus, the proposed TDFC is obtained by incorporating Tanimoto concept and deep fuzzy clustering. The proposed PRDOA with TDFC offered enhanced performance with the highest clustering accuracy of 89.8%, dice coefficient of 90%, Jaccard coefficient of 85.7%, and rand coefficient of 85.7%.