Elsevier

Pattern Recognition

Volume 128, August 2022, 108693
Pattern Recognition

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

https://doi.org/10.1016/j.patcog.2022.108693Get rights and content

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.

Abstract

Covid-19, what a strange, unpredictable mutated virus. It has baffled many scientists, as no firm rule has yet been reached to predict the effect that the virus can inflict on people if they are infected with it. Recently, many researches have been introduced for diagnosing Covid-19; however, none of them pay attention to predict the effect of the virus on the person's body if the infection occurs but before the infection really takes place. Predicting the extent to which people will be affected if they are infected with the virus allows for some drastic precautions to be taken for those who will suffer from serious complications, while allowing some freedom for those who expect not to be affected badly. This paper introduces Covid-19 Prudential Expectation Strategy (CPES) as a new strategy for predicting the behavior of the person's body if he has been infected with Covid-19. The CPES composes of three phases called Outlier Rejection Phase (ORP), Feature Selection Phase (FSP), and Classification Phase (CP). For enhancing the classification accuracy in CP, CPES employs two proposed techniques for outlier rejection in ORP and feature selection in FSP, which are called Hybrid Outlier Rejection (HOR) method and Improved Binary Genetic Algorithm (IBGA) method respectively. In ORP, HOR rejects outliers in the training data using a hybrid method that combines standard division and Binary Gray Wolf Optimization (BGWO) method. On the other hand, in FSP, IBGA as a hybrid method selects the most useful features for the prediction process. IBGA includes Fisher Score (FScore) as a filter method to quickly select the features and BGA as a wrapper method to accurately select the features based on the average accuracy value from several classification models as a fitness function to guarantee the efficiency of the selected subset of features with any classifier. In CP, 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 after performing the previous two phases. CPES has been compared against recent related strategies in terms of accuracy, error, recall, precision, and run-time using Covid-19 dataset [1]. This dataset contains routine blood tests collected from people before and after their infection with covid-19 through a Web-based form created by us. 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.

Keywords

Covid-19
Prediction
Naïve Bayes
Prudential Expectation

Cited by (0)

Asmaa H. Rabie received a B. Sc. in Computers and Systems Engineering, with general grade Excellent with class honor in 2013. She got the master Degree in the area of load forecasting using data mining techniques in 2016 at Computers eng. and system dept, Mansoura University, Egypt. She got the Ph.D Degree in the area of load forecasting using data mining techniques in 2020 at Computers eng. and system dept, Mansoura University, Egypt. Her Interests (Programming Languages, Classification, Big Data, Data Mining, healthcare system, and Internet of Things), she is currently an lecturer in the faculty of Engineering, Mansoura University, Egypt.

E mail: [email protected]

Faculty of Engineering, Mansoura University, Mansoura, Egypt

Nehal A. Mansour received a B. Sc. in Computer Engineering and Control Systems department from Mansoura University, Egypt with general grade Excellent with class honor. She got the master Degree in the area of data mining and artificial intelligence at Computer Engineering and Control Systems from Mansoura University, Egypt.

E-mail: [email protected]

Ahmed I. Saleh received a B. Sc. in Computer Engineering and Control Systems from Mansoura University, Egypt with general grade Excellent. He got the master Degree and PhD Degree in the area of Mobile agent ad computing. His research interests include (Programming Languages, Networks and System Administration, and Database), he is currently a professor in the faculty of Engineering, Mansoura University.

E-mail: [email protected]

Po. Box: 35516

Ali Takieldeen (IEEE Senior Member) received the PhD degree in Electronics and Communications Engineering in “Encryption and Data Security in Digital Communication Systems”. He has a lot of publications in various international journals and conferences. His current research interests are in multimedia processing, wireless communication systems, and Field Programmable Gate Array (FPGA) applications.

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