Accurate detection of COVID-19 patients based on distance biased Naive Bayes (DBNB) classification strategy

COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naive Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS2) and Final Selection Stage (FS2). IS2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS2, which is the next stage of APSO. FS2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naive Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

COVID-19, a global infectious disease, requires early detection of infected patients for effective treatment and disease control. This paper introduces a new strategy called DBNB, which uses APSO to select informative features for accurate diagnosis of COVID-19 patients. Experimental results show that DBNB outperforms recent COVID-19 diagnose strategies in accuracy and time efficiency.