Expecting individuals’ body reaction to Covid-19 based on statistical Naïve Bayes technique

Highlights：

• A new strategy for predicting the behavior of the person's body if he has been infected with Covid-19, which is called Covid-19 Prudential Expectation Strategy (CPES) is introduced.

• CPES has the ability to classify people based on their bodies’ reaction to Covid-19 infection, which is built upon a proposed Statistical Naïve Bayes (SNB) classifier.

• For enhancing the classification accuracy, CPES employs two proposed techniques for outlier rejection and feature selection, which are called Hybrid Outlier Rejection (HOR) method and Improved Binary Genetic Algorithm (IBGA) method respectively.

• HOR rejects outliers in the training data using a hybrid method that combines standard division and Binary Gray Wolf Optimization (BGWO) methods.

• On the other hand, IBGA selects the most useful features for the prediction process using hybrid method that consists of Fisher Score (FScore) as a fast method and BGA as an accurate method that depends on the average accuracy value from several classification models as a fitness function.

• CPES has been compared against recent related technologies for Covid-19 diagnosing.

• CPES outperforms the competing methods in experimental results because it provides the best results with values of 0.87, 0.13, 0.84, and 0.79 for accuracy, error, precision, and recall.

摘要

•A new strategy for predicting the behavior of the person's body if he has been infected with Covid-19, which is called Covid-19 Prudential Expectation Strategy (CPES) is introduced.•CPES has the ability to classify people based on their bodies’ reaction to Covid-19 infection, which is built upon a proposed Statistical Naïve Bayes (SNB) classifier.•For enhancing the classification accuracy, CPES employs two proposed techniques for outlier rejection and feature selection, which are called Hybrid Outlier Rejection (HOR) method and Improved Binary Genetic Algorithm (IBGA) method respectively.•HOR rejects outliers in the training data using a hybrid method that combines standard division and Binary Gray Wolf Optimization (BGWO) methods.•On the other hand, IBGA selects the most useful features for the prediction process using hybrid method that consists of Fisher Score (FScore) as a fast method and BGA as an accurate method that depends on the average accuracy value from several classification models as a fitness function.•CPES has been compared against recent related technologies for Covid-19 diagnosing.•CPES outperforms the competing methods in experimental results because it provides the best results with values of 0.87, 0.13, 0.84, and 0.79 for accuracy, error, precision, and recall.