Deming least square regressed feature selection and Gaussian neuro‐fuzzy multi‐layered data classifier for early COVID prediction

Abstract

Coronavirus disease (COVID‐19) is a harmful disease caused by the new SARS‐CoV‐2 virus. COVID‐19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID‐19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro‐fuzzy multi‐layered data classification (LSRGNFM‐LDC) technique is introduced in this article. LSRGNFM‐LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro‐fuzzy classifier in LSRGNFM‐LDC technique performs the data classification process with help of fuzzy if‐then rules for performing prediction process. Finally, the fuzzy if‐then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM‐LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm

“…They analysed the data of the West African countries and discovered many characteristics of the disease for each country. Mydukuri et al (2021) proposed a model that combines filter based feature selection with a neuro-fuzzy classifier for early COVID prediction. Their model improves performance in terms of accuracy and prediction time.…”

Quantum Edge Extraction of Chest CT Image for the Detection and Differentiation of Infected Region of COVID-19 Patient

“…But there has been no reduction in the complexity of time and space. Gaussian neuro-fuzzy multilayered data classifier has already been reported for early COVID prediction using Novel Corona Virus 2019 Dataset [ 16 ]. But this technique reveals no diagnosis of COVID-19 patients.…”

Special issue editorial on emerging trends in internet of things for e‐health and medical supply chain systems

“…This is of particular concern as high-performance computing architectures and smart algorithms are becoming widespread in e-health services: these advanced technologies need to be implemented with an ethical focus, such as making sensitive data anonymous and offering more checks or services to comply with legal framework and regulations, such as GDPR.This special issue is focused on advancements in digital technologies and IoT for e-health and medical supply chain systems. It contributes state-of-the-art research and applications in e-health records management, distribution, analysis, technology development and prototypes for ensuring efficiency, privacy and trust in real-world implementations, especially in large-scale computing environments.Through a careful review and selection process, the following articles have been included-they are all of the high quality and high academic rigour, and embody novel contributions, as per the Journal's standards.The first article, by Mydukuri et al (2022), proposes a novel technique known as least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC), which provides improved accuracy in COVID-19 early prediction based on various symptoms featuring in collected data. The technique performs with better accuracy and lower time consumption, with pre-processing applied to each input feature to eradicate irrelevant data.…”

“…The first article, by Mydukuri et al (2022), proposes a novel technique known as least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC), which provides improved accuracy in COVID-19 early prediction based on various symptoms featuring in collected data. The technique performs with better accuracy and lower time consumption, with pre-processing applied to each input feature to eradicate irrelevant data.…”

“…Based on the selected authors' work, guest editors have summed up and suggested the following areas for futuristic research focuses: Innovation-driven by COVID-19: Based on the development work byMydukuri et al (2022) andSood and Rawat (2022), innovative services and platforms were designed and implemented due to COVID-19. Researchers have consistently searched for new ways to improve our work efficiently and provide services effectively.…”