Multiple Efficient Data Mining Algorithms with Genetic Selection for Prediction of SARS-CoV2

In this paper, a multiple efficient data mining algorithms with feature selection algorithm for the prediction of SARS- CoV2(covid) is presented. Multiple efficient data mining are composed of a set of algorithms, which are reliable and simple that used to generate many predictions (positive, negative) under various conditions such as random forest, support vector machine, and logistic regression. In data mining, feature selection is a key step in the pre-processing of data. The basic premise of feature selection appears to be to select a subset of potential features by removing characteristics that have little predictive value as well as features that are highly correlated and redundant. Selection of significant features from COVID data is accomplished using genetic feature selection techniques. The final prediction can be improved by combining data mining techniques with a genetic feature selection algorithm in an intelligent method. It looks like the simulations made good guesses about the values in the validation data set in terms of precision, recall, F-rneasure, and accuracy. In fact, the suggested model's prediction errors are much smaller than those of traditional methods.