A novel hybrid approach for feature selection enhancement: COVID-19 case study

Feature selection is a promising Artificial Intelligence technique for screening, analysing, predicting, and tracking current COVID-19 patients and likely future patients. Significant applications are developed to track data of confirmed, recovered, and death cases. In this work, we propose a new feature selection method based on a new way of hybridization between filter and wrapper methods. The proposed approach is expected to achieve high classification accuracy with a small feature subset. Specifically, the main contribution of this work is a four steps-based approach organized as follows: First, we remove consecutively duplicate and constant features. Then, we select the highest-ranked feature with Mutual Information. In the last step, we run the 'Backward Feature Elimination' algorithm to delete features from the active subset until a stopping criterion based on the degradation of classification performance is met. We applied the proposed approach to a COVID-19 dataset to test its ability to find the relevant feature for characterizing the disease, such as new cases infected with the virus, people vaccinated, and the number of deaths, to better assess the situation. For evaluation purposes, experiments are conducted at the first stage on the COVID-19 dataset, then on six benchmark datasets that have a high dimensional and large size. The method performance is tracked and measured on these datasets and a comparison with many approaches is provided.