A new Covid-19 diagnosis strategy using a modified KNN classifier.

Covid-19 is a very dangerous disease as a result of the rapid and unprecedented spread of any previous disease. It is truly a crisis that threatens the world since its first appearance in December 2019 until our time. Due to the lack of a vaccine that has proved sufficiently effective so far, the rapid and more accurate diagnosis of this disease is extremely necessary to enable the medical staff to identify infected cases and isolate them from the rest to prevent further loss of life. In this paper, Covid-19 diagnostic strategy (CDS) as a new classification strategy that consists of two basic phases: Feature selection phase (FSP) and diagnosis phase (DP) has been introduced. During the first phase called FSP, the best set of features in laboratory test findings for Covid-19 patients will be selected using enhanced gray wolf optimization (EGWO). EGWO combines both types of selection techniques called wrapper and filter. Accordingly, EGWO includes two stages called filter stage (FS) and wrapper stage (WS). While FS uses many different filter methods, WS uses a wrapper method called binary gray wolf optimization (BGWO). The second phase called DP aims to give fast and more accurate diagnosis using a hybrid diagnosis methodology (HDM) based on the selected features from FSP. In fact, the HDM consists of two phases called weighting patient phase (WP2) and diagnostic patient phase (DP2). WP2 aims to calculate the belonging degree of each patient in the testing dataset to class category using naïve Bayes (NB) as a weight method. On the other hand, K-nearest neighbor (KNN) will be used in DP2 based on the weights of patients in the testing dataset as a new training dataset to give rapid and more accurate detection. The suggested CDS outperforms other strategies according to accuracy, precision, recall (or sensitivity) and F-measure calculations that are equal to 99%, 88%, 90% and 91%, respectively, as showed in experimental results.

Accurate detection of COVID-19 patients based on distance biased Naïve 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 Naïve 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 Naïve 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.

Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.

COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test.

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

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.

Soybean isoflavone affects in rabbits: Effects on metabolism, antioxidant capacity, hormonal balance and reproductive performance.

Though soybean isoflavones (SBI) have pharmaceutical properties, the compounds also have endocrine disrupting activities that may adversely affect fertility of mammals. The effects of SBI on metabolism, antioxidant capacity, hormonal balance and reproductive performance of male rabbits were investigated. Adult male rabbits (n = 21) fed an isoflavone-free diet were orally treated with 0 (control; CON), 5 (small; LSBI) or 20 (large; HSBI) mg of SBI/kg body weight/day for 12 weeks. Both SBI doses resulted in lesser blood plasma total protein concentrations, while there were no effects on glucose and cholesterol concentrations compared to CON. The HSBI-treated males had the greatest (P < 0.05) blood plasma total antioxidant capacity and least malondialdehyde. Treatment with both SBI doses induced a 43% increase in triiodothyronine concentrations (P < 0.05) and 82% in reaction times (P < 0.001), while decreased sperm concentrations (P = 0.01) and blood plasma testosterone concentrations (P = 0.017) 26% and 19%, respectively. The total functional sperm fraction was less (P < 0.05) in the HSBI group; however, there was no effect of the LSBI treatment as compared to values for the CON group. The kindling rates of females mated to HSBI-treated males tended to be less (P = 0.081) than those of does mated with LSBI or CON males. In conclusion, only the HSBI treatment improved antioxidant status; whereas, treatment with both LSBI and HSBI doses induced a hormonal imbalance which led to an impaired testis function indicating the sensitivity of the adult male reproductive system to SBI actions.

Prolonged exposure of dietary phytoestrogens on semen characteristics and reproductive performance of rabbit bucks.

The effects of inclusion of different sources of dietary phytoestrogens on antioxidant capacity, hormonal balance, libido, semen quality, and fertility of rabbit bucks were studied. Twenty-one, adult, fertile, V-line bucks were randomly allocated into 3 homogenous groups (n = 7/treatment) and received control diet (phytoestrogens-free diet, CON) or soybean meal isoflavones-containing diet (SMI) or linseed meal lignans-containing diet (LML) for 12 wk. The diets were formulated to be isocaloric and isonitrogenous. The concentrations of isoflavones in the SMI diet were 24.04 mg/100 g dry matter (DM) daidzein and 13.10 mg/100 g DM genistein. The major phytoestrogen detected in the LML diet was secoisolariciresinol (36.80 mg/100 g DM). Treatment had no effects on body weight, feed intake and rectal temperature of bucks. Compared with control, bucks fed the SMI and LML diets had higher (P < 0.001) blood plasma total antioxidant capacity (0.98 ± 0.12, 1.50 ± 0.13, and 2.29 ± 0.17 mM/L for CON, SMI, and LML, respectively), and lower (P < 0.01) blood plasma malondialdehyde (2.76 ± 0.23, 1.76 ± 0.16, and 1.70 ± 0.18 nmol/mL for CON, SMI, and LML, respectively), whereas activities of reduced glutathione and superoxide dismutase (SOD) enzymes were not affected. Bucks fed the SMI and LML diets had greater (P < 0.001) concentrations of blood plasma triiodothyronine. Feeding the SMI and LML diets decreased (P < 0.01) libido (8.26 ± 0.71, 12.18 ± 0.97, and 14.12 ± 1.12 s for CON, SMI, and LML, respectively), sperm concentration (327.7 ± 21.6, 265.8 ± 36.8, and 226.5 ± 20.1 × 106/mL for CON, SMI, and LML, respectively), testosterone (5.16 ± 0.95, 3.91 ± 0.63, and 3.04 ± 0.92 ng/mL for CON, SMI, and LML, respectively), and seminal plasma fructose compared with the CON diet. The percentage of progressive motile sperm was improved (P < 0.001) by both phytoestrogen-containing diets. Feeding the SMI diet increased (P = 0.02) the percentage of live sperm compared with CON, whereas LML resulted in an intermediate value. Dietary treatment of bucks did not affect kindling rates or litter sizes of does, and did not affect birth weights or viabilities of kits. In conclusion, prolonged consumption of dietary isoflavones or lignans did not impair semen fertilizability. This may be due to the benefits of antioxidant activity or due to the benefits of other components in the diet. Dietary phytoestrogens did evoke obvious decreases in libido and steroidogenesis with altered semen parameters.