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The Covid-19 outbreak, which emerged in 2020, became the top priority of the world. The fight against this disease, which has caused millions of people’s deaths, is still ongoing, and it is expected that these studies will continue for years. In this study, we propose an improved learning model to predict the severity of the patients by exploiting a combination of machine learning techniques. The proposed model uses an adaptive boost algorithm with a decision tree estimator and a new parameter tuning process. The learning ratio of the new model is promising after many repeated experiments are performed by using different parameters to reduce the effect of selecting random parameters. The proposed algorithm is compared with other recent state-of-the-art algorithms on UCI data sets and a recent Covid-19 dataset. It is observed that competitive accuracy results are obtained, and we hope that this study unveils more usage of advanced machine learning approaches.

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Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. Teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.