Healthcare Informatics Challenges: A Medical Diagnosis Using Multi Agent Coordination-Based Model for Managing the Conflicts in Decisions

Elghamrawy, Sally M.

doi:10.1007/978-3-030-58669-0_32

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Studies [24][25][26][27][28][29][30] have investigated various techniques for selecting the most significant features in different fields. The FS phase in AIMDP uses the WOA algorithm to select the most significant features from detailed patient information to distinguish COVID-19 disease from other vital phonemes.…”

Section: Feature Selection Phase Based On Woa (Fswoa)mentioning

confidence: 99%

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Elghamrawy¹,

Hassnien²,

Snåšel³

2021

Computers, Materials &Amp; Continua

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Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic's additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization algorithm (WOA) to select the most relevant patient signs. A set of experiments validated AIMDP performance. It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic (AUC-ROC) curve, positive predictive value (PPV), negative predictive rate (NPR) and negative predictive value (NPV). AIMDP was applied to a dataset of hundreds of real data and CT images, and it was found to achieve 96% AUC for diagnosing COVID-19 and 98% for overall accuracy. The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

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Section: Feature Selection Phase Based On Woa (Fswoa)mentioning

confidence: 99%

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Elghamrawy¹,

Hassnien²,

Snåšel³

2021

Computers, Materials &Amp; Continua

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“…As a rule, metaheuristic algorithms are evaluated based on their ability to solve decision-making difficulties and their significant balance between exploring and exploiting [9][10][11][12][13][14]. Exploitation refers to the capacity to find better alternatives to well-known answers.…”

Section: Introductionmentioning

confidence: 99%

A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

Fadel

Elseddeq

Arnous³

et al. 2022

IEEE Access

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The mortality rate of cancer is among the highest in the world. One death occurs every six in the world. Both machine learning (ML) and deep learning (DL) have been used by scientists to predict cancer. In addition, DL can analyze a huge amount of healthcare data in a short period of time to study the chances of recurrence, progression and patient survival. An accurate and quick framework for improving cancer prognosis prediction is presented in this study. A fast and accurate optimizer is necessary to predict both critical and non-critical cases, so a modified binary version of the Whale Optimization Algorithm (WOA) is proposed. Based on sigmoid transfer functions, this version identifies the subset of features that is minimally optimal while maximizing classification accuracy. This framework is composed of an optimized parameter Long-Short Term Memory (LSTM) Neural Network, with the input being the optimal set of feature selection layer. The proposed framework performs better than previous frameworks having an average accuracy of 100% and an execution time of 4113 seconds.

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An Intelligent Cloud Computing Context-Aware Model for Remote Monitoring COVID-19 Patients Using IoT Technology

Waleed

Elghamrawy

2021

Studies in Systems, Decision and Control

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Healthcare Informatics Challenges: A Medical Diagnosis Using Multi Agent Coordination-Based Model for Managing the Conflicts in Decisions

Cited by 3 publications

References 19 publications

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

An Intelligent Cloud Computing Context-Aware Model for Remote Monitoring COVID-19 Patients Using IoT Technology

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