A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Abstract

COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS²) and Accurate Selection Stage (AS²). FS²relies on filter, while AS² uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. Extensive experiments have been done considering the proposed detection strategy as well as recent competitive techniques on the chest CT images. Experimental results have shown that the proposed detection strategy outperforms recent techniques as it introduces the maximum accuracy rate.

Keywords: COVID-19; Classification; Feature selection; KNN.

Fig. 1

A graphic representation of the rapid spike in infections.

Fig. 2

Different types of KNN trapping.

Fig. 3

Corona patients detection strategy.

Fig. 4

The sequential steps of HFSM method.

Fig. 5

EKNN training phase.

Fig. 6

Illustrative example showing the followed steps during EKNN testing phase.

Fig. 7

Accuracy of features selection methods using NB.

Fig. 8

Error of features selection methods using NB.

Fig. 9

Precision of features selection methods using NB.

Fig. 10

Sensitivity of features selection methods using NB.

Fig. 11

Macro-average of precision for features selection methods using NB.

Fig. 12

Macro-average of recall for features selection methods using NB.

Fig. 13

Micro-average of precision for features selection methods using NB.

Fig. 14

Micro-average of recall for features selection methods using NB.

Fig. 15

F-Measure of the different features selection methods using NB.

Fig. 16

Run time of the different features selection methods using NB.

Fig. 17

Accuracy of the different classification techniques.

Fig. 18

Error of the different classification techniques.

Fig. 19

Precision of the different classification techniques.

Fig. 20

Sensitivity of the different classification techniques.

Fig. 21

Macro-average precision of the different classification techniques.

Fig. 22

Macro-average recall of the different classification techniques.

Fig. 23

Micro-average precision of the different classification techniques.

Fig. 24

Micro-average recall of the different classification techniques.

Fig. 25

F-Measure of the different classification techniques.

Fig. 26

Run time of the different classification techniques.