Prediction of COVID-19 Cases Using Constructed Features by Grammatical Evolution

Abstract

A widely used method that constructs features with the incorporation of so-called grammatical evolution is proposed here to predict the COVID-19 cases as well as the mortality rate. The method creates new artificial features from the original ones using a genetic algorithm and is guided by BNF grammar. After the artificial features are generated, the original data set is modified based on these features, an artificial neural network is applied to the modified data, and the results are reported. From the comparative experiments done, it is clear that feature construction has an advantage over other machine-learning methods for predicting pandemic elements.

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

“…Grammatical Evolution is an evolutionary algorithm, used to produce valid programs in any language defined by a BNF grammar, and it has been used in a variety of cases, such as solving trigonometric identities [49], automatic composition of music [50], combinatorial optimization problems [51], etc. The feature construction method was initially proposed by Gavrilis et al [52] and was applied in many real world problems, such as classification of EEG signals [53], prediction of COVID-19 cases [54], Hemiplegia type detection [55], etc. The feature construction method creates artificial features from the original ones through Grammatical Evolution, and every set of potential features is evaluated on the training set using a machine learning method.…”

“…This section reports on the assessment of the proposed FC2RBF technique's efficacy in creating artificial features for feature learning and class prediction using the three datasets from the SmartSpeech project (see Section 3). These issues have been extensively examined by numerous scholars in the pertinent academic discourse, encompassing a diverse array of research domains spanning from economics to health [3,40,54,55,73,74].…”