Prediction of COVID-19 Individual Susceptibility using Demographic Data: A Case Study on Saudi Arabia

Althnian, Alhanoof; Elwafa, Afnan Abou; Aloboud, Nourah; Alrasheed, Hend; Kurdi, Heba

doi:10.1016/j.procs.2020.10.051

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Althnian et al [5] conducted a study to forecast the susceptibility of individuals based on demographic data, including age, gender, nationality, and location. The dataset was obtained from the Saudi Ministry of Health.…”

Section: Studies Examining Eurasian Casesmentioning

confidence: 99%

A Survey of Recent Studies on COVID-19 Outbreak Prediction Using Statistical and Machine Learning Methods

ÇETİN¹,

Abut²

2022

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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Section: Studies Examining Eurasian Casesmentioning

confidence: 99%

A Survey of Recent Studies on COVID-19 Outbreak Prediction Using Statistical and Machine Learning Methods

ÇETİN¹,

Abut²

2022

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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“…COVID‐19 is an acute respiratory problem producing symptoms like fever, cough, and breathing problem. Unfortunately, lack of proper medical treatment of this disease motivates the researchers for early diagnosis 1 . Moreover, it is a contagious disease which can be spread through person to person by coughing, splitting and sneezing.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, lack of proper medical treatment of this disease motivates the researchers for early diagnosis. 1 Moreover, it is a contagious disease which can be spread through person to person by coughing, splitting and sneezing. Early detection and isolation of the affected person is highly required to break this spreading chain.…”

mentioning

confidence: 99%

Deep‐CoV: An integrated deep learning model to detect COVID‐19 using chest X‐ray and CT images

Roy

Das

2023

Computational Intelligence

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The COVID-19 virus has fatal effect on lung function and due to its rapidity the early detection is necessary at the moment. The radiographic images have already been used by the researchers for the early diagnosis of COVID-19. Though several existing research exhibited very good performance with either x-ray or computer tomography (CT) images, to the best of our knowledge no such work has reported the assembled performance of both x-ray and CT images. Thus increase in accuracy with higher scalability is the main concern of the recent research. In this article, an integrated deep learning model has been developed for detection of COVID-19 at an early stage using both chest x-ray and CT images. The lack of publicly available data about COVID-19 disease motivates the authors to combine three benchmark datasets into a single dataset of large size. The proposed model has applied various transfer learning techniques for feature extraction and to find out the best suite. Finally the capsule network is used to categorize the sub-dataset into COVID positive and normal patients. The experimental results show that, the best performance exhibits by the ResNet50 with capsule network as an extractor-classifier pair with the combined dataset, which is composed of 575 numbers of x-ray images and 930 numbers of CT images. The proposed

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“…For instance, some journals showed that men are more easily affected to the disease than women [3] , whereas in others no significant difference in gender-related [3] . Zhang et al [3] claimed that children aged less than 14 years were less affected to the disease compared to age between 15 to 64 years, and elderly people aged more than 65 years were more affected.…”

Section: Introductionmentioning

confidence: 99%

“…Many models are used to predict the influenced of different disease like Diabetics, Cancer, Asthma, etc [3] . One of the best models is machine learning.…”

Section: Introductionmentioning

confidence: 99%

A Covid-19 Prediction Based on Machine Learning Algorithms – A Literature Review

AGUSTHIYAR¹,

SARANYA²

2022

YMER

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The SAR.CoV2 disease 2019(covid-19) pandemic affected many countries of the world. Actually, almost all the countries presented Covid-19 positive cases and governments are choosing different health policies to stop the infection and many programs are conducted with aware common people. Then number of positive cases increasing rapidly everyday around the world. This paper is going to propose a prediction on what basis common people getting affected and how to reduce the spreading of disease. Machine learning algorithms have been used in all the fields in predicting. Especially in medicine and enriches the applications of machine learning which are accurate and robust in selecting attributes. Here we investigate some of the machine learning models namely Decision Tree, Random Forest, Adaboost and Logistic Regression to predict accuracy of getting affected. In our experiment shows prediction result accuracies 70.1, 70.3, 67.9, 70.6 respectively. Keywords: SARS, Decision tree, Random Forest, Logistic Regression, AdaBoost, Renal Chronic, Intubed

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Prediction of COVID-19 Individual Susceptibility using Demographic Data: A Case Study on Saudi Arabia

Cited by 7 publications

References 25 publications

A Survey of Recent Studies on COVID-19 Outbreak Prediction Using Statistical and Machine Learning Methods

A Survey of Recent Studies on COVID-19 Outbreak Prediction Using Statistical and Machine Learning Methods

Deep‐CoV: An integrated deep learning model to detect COVID‐19 using chest X‐ray and CT images

A Covid-19 Prediction Based on Machine Learning Algorithms – A Literature Review

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