MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches

Abdulkareem, Karrar Hameed; Al-Mhiqani, Mohammed Nasser; Dinar, Ahmed M.; Mohammed, Mazin Abed; Al-Imari, Mustafa Jawad; Al-Waisy, Alaa S.; Alghawli, Abed Saif; Al-qaness, Mohammed A. A.

doi:10.3390/bioengineering9090457

Cited by 10 publications

(3 citation statements)

References 57 publications

(62 reference statements)

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“…Basic performance indicators including the R , RMSE , MAPE , MAE , NS , and a20-index were taken into account while evaluating the effectiveness of the CF and ANN models in order to determine the models’ reliability [ 64 , 65 , 66 , 67 , 68 ]. It is clear that COVID-19 has affected many areas of our life, including our environment, activities, and other factors, and may require intelligent solutions using AI techniques, medical images, and clinical data to control the pandemic [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. Formulas (11) to (16) below give the equations for the performance indices listed above.…”

Section: R-event Prognosismentioning

confidence: 99%

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Kapoor

Kumar

et al. 2022

IJERPH

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The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

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Section: R-event Prognosismentioning

confidence: 99%

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Kapoor

Kumar

et al. 2022

IJERPH

Self Cite

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“…Early detection of positive cases is an impressive method to control the outbreak rate ( Abdulkareem et al, 2022 ). The conventional way to detect COVID-19 is a real-time reverse transcription-polymerase chain reaction (RT-PCR).…”

Section: Introductionmentioning

confidence: 99%

A novel framework based on deep learning for COVID-19 diagnosis from X-ray images

Javadi-Moghaddam¹

2023

PeerJ Computer Science

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Background The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. Methods This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Results Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. Conclusion This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.

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“…Abdulkareem et al [ 28 ] proposed to provide a multidimensional examination framework (MEF) based on integrated multi-criteria decision-making (MCDM) techniques for prioritizing individuals seriously affected by COVID-19. The recommended methodology for the multidimensional examination framework contained many multivariate evaluation parameters, including demographic, analytical results, vital signs, feelings, and chronic illnesses, in contrast to the current research, which only addressed one aspect of the evaluation variables.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features

Choudhry,

Qureshi,

Aurangzeb

et al. 2023

Biomimetics

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A recently discovered coronavirus (COVID-19) poses a major danger to human life and health across the planet. The most important step in managing and combating COVID-19 is to accurately screen and diagnose affected people. The imaging technology of lung X-ray is a useful imaging identification/detection approach among them. The help of such computer-aided machines and diagnoses to examine lung X-ray images of COVID-19 instances can give supplemental assessment ideas to specialists, easing their workload to some level. The novel concept of this study is a hybridized approach merging pertinent manual features with deep spatial features for the classification of COVID-19. Further, we employed traditional transfer learning techniques in this investigation, utilizing four different pre-trained CNN-based deep learning models, with the Inception model showing a reasonably accurate result and a diagnosis accuracy of 82.17%. We provide a successful diagnostic approach that blends deep characteristics with machine learning classification to further increase clinical performance. It employs a complete diagnostic model. Two datasets were used to test the suggested approach, and it did quite well on several of them. On 1102 lung X-ray scans, the model was originally evaluated. The results of the experiments indicate that the suggested SVM model has a diagnostic accuracy of 95.57%. When compared to the Xception model’s baseline, the diagnostic accuracy had risen by 17.58 percent. The sensitivity, specificity, and AUC of the proposed models were 95.37 percent, 95.39%, and 95.77%, respectively. To show the adaptability of our approach, we also verified our proposed model on other datasets. Finally, we arrived at results that were conclusive. When compared to research of a comparable kind, our suggested CNN model has a greater accuracy of classification and diagnostic effectiveness.

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MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches

Cited by 10 publications

References 57 publications

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

A novel framework based on deep learning for COVID-19 diagnosis from X-ray images

Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features

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