A comprehensive evaluation of Marine predator chaotic algorithm for feature selection of COVID-19

The COVID-19 disease has spread very swiftly in different parts of the world. Some of the implications of this disease are loss of life, health-related issues, negative impact on the economy, and several other social issues. For the purpose of forecasting this disease's spread, various algorithms have been employed. Also, the application of several metaheuristic optimization algorithms has been explored for choosing optimal features from a big data set. This paper addresses this issue and proposes a chaotic algorithm based on Marine Predator Algorithm (MPA). A normalized fusion of chaotic function-is first proposed. The function is based on beta chaotic map. Based on this function, position update mechanism is developed for improving the performance of the original MPA. The developed algorithm is named as Marine Predator Chaotic Algorithm (MPCA). The COVID-19 dataset has been employed for judging the efficacy of the proposed algorithms. Different statistical analyses and graphical visualizations affirm the efficacy of the proposed algorithms.