COX Regressive Winsorized Correlated Convolutional Deep Belief Boltzmann Network for Covid-19 Prediction with Big Data

Big data analytics in the health care industry is a very promising method of integrating, discovering, and analyzing huge volume of complex heterogeneous data. Some of the techniques were not well suited for medical data classification with minimum time consumption. To improve disease prediction accuracy, a novel technique Cox Regressive Winsorized Correlated Convolutional Deep Belief Boltzmann Network (CRWCCDBBN) is introduced. The proposed CRWCCDBBN technique uses multiple layers for performing the different processes like feature selection and classification. Initially, feature selection is performed using Cox Regression to minimize the dimensionality of the data by finding the Cox partial log-likelihood between the two features. After the feature selection, the classification is performed using Winsorized Correlation coefficient by analyzing the training and testing disease data. Based on the classification results, Covid 19 disease is correctly diagnosed with a minimum error by updating the weight value. Finally, the gradient descent first-order iterative function is used to find the local minimum error. Experimental evaluation of CRWCCDBBN technique is carried out using a Covid 19 Dataset with different performance metrics such as accuracy, precision, recall, f-measure, and prediction time with respect to a number of patient data. The observed result proves that the proposed CRWCCDBBN technique achieves higher prediction accuracy with a minimum time consumption than the state-of-the-art methods.