Knowledge Based Expert System for Diagnosis of COVID-19

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Artificial Intelligence Assisted Decision Making in Predicting COVID-19 Patient's Path

Artificial Intelligence (AI) has made significant advances in all aspects of healthcare. Different studies have been investigated using AI regarding improve patient diagnosis, emergency care, and patient safety for different diseases. The coronavirus disease (COVID-19) has escalated into a global public health emergency. Different have shown clinical decisions for COVID-19 on diagnosis accuracy, severity risk, and so on. Moreover, few studies have focussed on clinical decisions for COVID-19. The fact global pandemic of corona poses a big challenge for clinicians, this research intends to detect a coronavirus patient path based on the virus' biological traits and presents an adequate mechanism for the efficient decision support system that assists doctors in predicting a COVID-19 patient. To train and test our model, we used 311 patient data which are 214 (69%) male and 96 (31%) female. Data were collected and maintained from three centers of Ethiopian Hospitals from Nov 2021 to March 2022 with the age range of 21 up to 67. The experiments were performed with three Machine Learning (ML) algorithms, namely Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The model was validated using input variables (n=10) which achieved better involvement in the virus symptoms identification and based on the evaluation metrics, ANN achieved 97%, 96%, 85%, and 98.3% for recall, precision, and F1- measures respectively. Our results demonstrated that the SVM algorithm achieved a 91.3% average accuracy and the other two methods had 87.75% and 96.05% respectively. In this research, ANN does better than the NB classifier by 8.3% on average and better than SVM by 4.75%. In addition, using more prediction algorithms and a larger dataset includes more parameters used to estimate patients. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

X-Ray image-based COVID-19 detection using deep learning

COVID-19 is a type of respiratory infection that primarily affects the lungs. Obtaining a chest X-ray is one of the most important steps in detecting and treating COVID-19 occurrences. Our study's goal is to detect COVID-19 from chest X-ray images using a Convolutional Neural Network (CNN). This study presents an effective method for categorizing chest X-ray images as Normal or COVID-19 infected. We used CNN, activation functions dropout, batch normalization, and Keras parameters to build this model. The classification method was implemented using open source tools "Python" and "OpenCV," both of which are freely available. The acquired images are transmitted through a series of convolutional and max pooling layers activated with the Rectified Linear Unit (ReLU) activation function, and then fed into the neurons of the dense layers, and finally activated with the sigmoidal function. Thereafter, SVM was used for classification using the knowledge from the learning model to classify the images into a predefined class (COVID-19 or Normal). As the model learns, its accuracy improves while its loss decreases. The findings of the study indicate that all models produced promising results, with augmentation, image segmentation, and image cropping producing the most efficient results, with a training accuracy of 99.8% and a test accuracy of 99.1%. As a result, the findings show that deep features provided consistent and reliable features for COVID-19 detection. Therefore, the proposed method aids in faster diagnosis of COVID-19 and the screening of COVID-19 patients by radiologists.

Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients.

COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus disease of humans. The COVID-19 pandemic has spread to every country on the planet and has wreaked havoc on these countries by increasing the number of human deaths, and in addition, caused intense hunger, and lowered economic productivity. Due to a lack of sufficient radiologist, a restricted amount of COVID-19 test kits is available in hospitals, and this is also accompanied by a shortage of equipment due to the daily increase in cases, as a result of increase in the number of persons infected with COVID-19 . Even for experienced radiologists, examining chest X-rays is a difficult task. Many people have died as a result of inaccurate COVID-19 diagnosis and treatment, as well as ineffective detection measures. This paper, therefore presents a unique detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients. To achieve quick diagnosis, a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method is proposed in this paper to help medical experts diagnose COVID-19 disease. The diagnostic performance of the hybrid CNN model and HOG-based method was then evaluated using chest X-ray images collected from University of Gondar and online databases. The experiment was performed using Keras (with TensorFlow as a backend) and Python. After the DCCNet model was evaluated, a 99.9% training accuracy and 98.3% test accuracy was achieved, while a 100% training accuracy and 98.5% test accuracy was achieved using HOG. After the evaluation, the hybrid model achieved 99.97% and 99.67% training and testing accuracy for detection and classification of COVID-19 which was better by 1.37% compared to when features were extracted using CNN and 1.17% when HOG was used. The DCCNet achieved a result that outperformed state-of-the-art models by 6.7%.