RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing

El-Rashidy, Nora; ElSayed, Nesma E.; El-Ghamry, Amir; Talaat, Fatma M.

doi:10.1007/s00500-022-07420-1

Cited by 25 publications

(9 citation statements)

References 75 publications

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“…Therefore, it is necessary to propose machine and deep learning approaches based on blockchain and IoT technologies because both significantly leverage the global healthcare industry to timely detect and identify COVID-19 from the data generated by these devices. Furthermore, to generalize the proposed approach in detecting other important medical diseases [58,59], we aim to validate the performance of the proposed approach by training and testing it on the identification of brain tumors [60,61], pest detection [62], heart diseases [63,64], and mask detection [65], blood diseases [66][67][68].…”

Section: Discussionmentioning

confidence: 99%

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

et al. 2023

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Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

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Section: Discussionmentioning

confidence: 99%

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

et al. 2023

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“…Explainable artificial intelligence has penetrated healthcare classification approaches recently; for example, in early diagnosis of Parkinson’s disease [ 25 ]. Moreover, coupled with fog computing, explainable deep learning approach has been proposed for prediction of gestational diabetes in [ 26 ]. The work in this communication concentrates on use of explainable classification technique for classification of heart diseases.…”

Section: Related Workmentioning

confidence: 99%

XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI

Das

Sultana²,

Bhattacharya³

et al. 2023

J Supercomput

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Machine learning (ML) has been used for classification of heart diseases for almost a decade, although understanding of the internal working of the black boxes, i.e., non-interpretable models, remain a demanding problem. Another major challenge in such ML models is the curse of dimensionality leading to resource intensive classification using the comprehensive set of feature vector (CFV). This study focuses on dimensionality reduction using explainable artificial intelligence, without negotiating on accuracy for heart disease classification. Four explainable ML models, using SHAP, were used for classification which reflected the feature contributions (FC) and feature weights (FW) for each feature in the CFV for generating the final results. FC and FW were taken into account in generating the reduced dimensional feature subset (FS). The findings of the study are as follows: (a) XGBoost classifies heart diseases best with explanations, with an increase in 2% in model accuracy over existing best proposals, (b) explainable classification using FS exhibits better accuracy than most of the literary proposals, and (c) with the increase in explainability, accuracy can be preserved using XGBoost classifier for classifying heart diseases, and (d) the top four features responsible for diagnosis of heart disease have been exhibited which have common occurrences in all the explanations reflected by the five explainable techniques used on XGBoost classifier based on feature contributions. To the best of our knowledge, this is first attempt to explain XGBoost classification for diagnosis of heart diseases using five explainable techniques.

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“…Two data sets were used in this paper, the data was collected from hospital in Luzhou, China, this data set is split into two parts: the healthy people and diabetes affected people and another data set was used PIDD there have all patients are female were age is 21-year-old. Among these data set samples were randomly selected [15]. Three different classi cation algorithms Decision Tree, Random Forest and Neural Network were applied.…”

Section: Deepti Sisodia and Dilipmentioning

confidence: 99%

Early Stage Diabetes Prediction by Approach Using Machine Learning Techniques

Zarar

Wang

2023

Preprint

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Diabetes is the most viral and chronic disease throughout the world. A large number of people are affected by this chronic disease. Early detection of diabetes in a patient is crucial for ensuring a good quality of life. Machine learning techniques or Data Mining Techniques are playing a significant role in today’s life to detect diabetes and improve performance to make further accurate predictions. The aim of this research is diabetes prediction with the approach of machine learning techniques. In this technical approach, we have taken two data sets Pi-ma Indian diabetes data set and the Kaggle diabetes data set, and proposed a model for diabetes prediction. We have used four different machine learning algorithms such as Support Vector Machine, Decision Forest, Linear Regression, and Artificial Neural Network. In these machine learning algorithms, ANN gives the best prediction performance where the highest accuracy is 98.8% so, it could be used as an alternative method to support predict diabetes complication diseases at an initial stage. Further, this work can be extended to find how likely non-diabetic people can have diabetes in the next few years and also, this predicted model can be used for imaging processing in the future to find diabetes for the prediction of diabetic and non-diabetic.

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RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing

Cited by 25 publications

References 75 publications

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI

Early Stage Diabetes Prediction by Approach Using Machine Learning Techniques

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