ArticleApplied soft computing2021
Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Fuzzy Multi-Criteria Decision Analysis of Emergency System in COVID-19 Pandemic. An Extensive Narrative Review.International journal of environmental research and public health · 2021Pooled it
- Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification.Journal of healthcare informatics research · 2024Article
- DLAAD-deep learning algorithms assisted diagnosis of chest disease using radiographic medical images.Frontiers in medicine · 2024Article
- A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests.Health information science and systems · 2023Article
- Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices.Sensors (Basel, Switzerland) · 2023Article
- Diagnosis of Chest Pneumonia with X-ray Images Based on Graph Reasoning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Real-time face mask position recognition system based on MobileNet model.Smart health (Amsterdam, Netherlands) · 2023Article
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model.Expert systems with applications · 2023Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method.Applied soft computing · 2023Article
- A fuzzy approach to support decision-making in the triage process for suspected COVID-19 patients in Brazil.Applied soft computing · 2022Article
- Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels.Environmental science and pollution research international · 2022Article
- Expecting individuals' body reaction to Covid-19 based on statistical Naïve Bayes technique.Pattern recognition · 2022Article
- Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study.International journal of environmental research and public health · 2022Article
- Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks.Expert systems with applications · 2022Article
- Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.Computer methods and programs in biomedicine · 2022Article
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.Computers in biology and medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.