Adaptive Neuro-Fuzzy Inference System (ANFIS) for Rapid Diagnosis of COVID-19 Cases Based on Routine Blood Tests

Deif, Mohanad A.; Hammam, Rania E.; Solyman, Ahmed A. A.

doi:10.22266/ijies2021.0430.16

Cited by 23 publications

(25 citation statements)

References 24 publications

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“…Interpretability can be obtained through summary plots [ 23 , 32 ]. The SHAP summary plot shows how much each predictor contributes, either positively or negatively, to the target outcome variable (whether or not the patient needs admission to an ICU).…”

Section: Methodsmentioning

confidence: 99%

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Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Deif

Solyman

Alsharif

et al. 2021

Sensors

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The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.

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Section: Methodsmentioning

confidence: 99%

“…A set of classification or regression trees is used in XGBoost, which is based on DT ensembles [21]. It predicts a target variable using training data (with multiple features) [22,23].…”

Section: Introductionmentioning

confidence: 99%

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Deif

Solyman

Alsharif

et al. 2021

Sensors

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“…Deep learning techniques have become popular for diagnosing lung diseases based on chest radiographs since X-ray radiography is a low-cost imaging modality with many data for training machine learning algorithms. Traditional machine learning algorithms were used by several research groups [ 12 , 21 , 22 ] to distinguish normal patients from those with tuberculosis using CXR images. By adjusting CNN settings, deep machine learning techniques were applied to classify patients with tuberculosis [ 23 , 24 , 25 ]; using pre-trained models, transfer learning was used to detect patients with tuberculosis [ 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Baghdadi

Maklad

Malki

et al. 2022

Sensors

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Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.

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“…In this paper, GWO has been used among other optimization algorithms because the advantages of GWO are as follows [35,39]: easy to implement due to its simple structure; less storage and computation requirements; faster convergence due to continuous reduction in search space; fewer decision variables; and ability to avoid local minimums. With only two control parameters to adjust the performance of the algorithm, which insures better stability and avoids complexity.…”

Section: Multi-objective Grey Wolf Optimizermentioning

confidence: 99%

“…One part is used for training the regression model, while the other part is used for the final evaluation of the model. Data processing steps are shown in the following: (a) Exploratory Data Analysis (EDA) EDA is a common approach [39] for explaining the fundamental characteristics of a dataset by studying the characteristics, usually using visual methods. Histogram and the Interquartile Range (IQR) algorithm were employed to investigate and seek information about dataset artifacts.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

et al. 2021

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Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.

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Adaptive Neuro-Fuzzy Inference System (ANFIS) for Rapid Diagnosis of COVID-19 Cases Based on Routine Blood Tests

Cited by 23 publications

References 24 publications

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

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