scholarly journals COVID-19 Future Predictions Using Machine Learning Algorithms

2021 ◽  
pp. 102-110
Author(s):  
R. Saradha Devi ◽  
Dr. J. G. R. Sathiaseelan
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Short Term Memory ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Human Beings ◽  
The World ◽  
Health Organization ◽  
Deep Learning Neural Network

Corona Virus Infectious Disease (COVID-19) is an infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last year. COVID-19 was declared “Pandemic” by the World Health Organization (WHO) on March 11, 2020. This research proposed a method for confirming COVID-19 instances after doctors' diagnoses. The goal of this study is to see how similar the projected findings are to the original data in COVID-19 Confirmed-Negative-Released-Death situations using machine learning. This paper suggests a verification approach created on the Deep-learning Neural Network concept for this purpose. Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also used in this framework to train the dataset. The outcomes of the forecast match those predicted by clinical doctors.

scholarly journals Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

2020 ◽  
Vol 2 (3) ◽  
pp. 172-177
Author(s):  
Shawni Dutta ◽  
◽  
Samir Kumar Bandyopadhyay ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Original Data ◽  
World Health ◽  
Learning Approach ◽  
Human Beings ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
The World

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

Predicting the Linguistic Accessibility of Chinese Health Translations: Using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Pierrette Bouillon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Health Resources ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
Health Organization ◽  
C5.0 Decision Tree ◽  
Machine Learning Models

BACKGROUND Linguistic accessibility has important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been under-studied. OBJECTIVE Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. METHODS Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from China Centre for Disease Control and Prevention. RESULTS Pairwise adjusted t test showed that three new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of AUC: C5.0 decision tree (p=0.000, 95% CI: -0.249, -0.152), random forest (p=0.000, 95% CI: 0.139, 0.239) and XGBoost Tree (p=0.000, 95% CI: 0.099, 0.193). There was however no significant difference between C5.0 decision tree and random forest (p=0.513). Extreme gradient boost tree was the best model having achieved statistically significant improvement over the C5.0 model (p=0.003) and the Random Forest model (p=0.006) at the adjusted Bonferroni p value at 0.008. CONCLUSIONS The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

scholarly journals Prediction of Heart Disease using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2422-2429
Author(s):  
Aadar Pandita
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Efficient Manner ◽  
Heart Attacks ◽  
The World ◽  
Health Organization

Heart diseases have been the primary reason for death all over the world. Majority of the deaths related to cardiovascular problems are caused by heart attacks and strokes. The World Health Organization (WHO) indicates that an approximate 17.9 million people die due to such diseases every year. Therefore, it is essential that we find methods to ensure the minimization of these numbers. In order to minimize the detrimental effects of heart diseases, we must try to predict its presence at earlier stages. Machine Learning algorithms can help us effectively predict such results with a high degree of accuracy which can in turn help doctors and patients detect the onset of such diseases and reduce their impact or prevent them from occurring. Our objective is to create a system that is able to accurately determine the presence of heart disease in a time and cost efficient manner.

Dietary and living regimen of covid-19 with special reference to Ayurveda Varsha and Sharad Ritucharya

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 758-762
Author(s):  
Amit Biswas ◽  
KunalChandankhede
Keyword(s):  
Environmental Changes ◽  
World Health ◽  
Human Beings ◽  
Dietary Regimen ◽  
Global Pandemic ◽  
The World ◽  
Health Organization ◽  
Disease Free ◽  
Individual Disease ◽  
Morbid Conditions

Wuhan originated Covid-19 disease is caused by SARC-COV 2 virus. It is a contagious disease it spread all over the world. World health organization declared a global pandemic disease. In Covid-19 immunity plays an important role. In old age people or having other co-morbid conditions the mortality rate is more. Ayurveda has a big role in improved immunity or to intact immunity. The principle of Ayurveda is to keep individual swastha (diseases free). To maintain individual disease-free Ritucharya is one of the important subjects of Ayurveda. Aimed of study is to find out Ritucharya literature from the Ayurveda and modern research specifically Varsha and Sharad ritu. Ritucharya contains dietary regimen, living modification, common medicine, and contraindicated things those changing according to environmental change. Upcoming season in India is Varsha and Sharad ritu. Environmental changes are huge in this season and it directly affected human beings. So this study reveals property of ritu, dietary regimen, living modification, common medicine and contraindicated things in upcoming varsha and sharad ritu.

scholarly journals Deafening silence on a vital issue: The World Health Organization has ignored the sexuality of persons with disabilities

2018 ◽  
Vol 7 ◽  
Author(s):  
Christine Peta
Keyword(s):  
World Health Organization ◽  
Well Being ◽  
World Health ◽  
Human Beings ◽  
Persons With Disabilities ◽  
The Public ◽  
National Priority ◽  
Social Welfare System ◽  
The World ◽  
Health Organization

In 2016, the World Health Organization, through the Global Cooperation on Assistive Technology Initiative, issued the Priority Assistive Products List which is meant to be a guide to member states of the 50 assistive products needed for a basic health care and/or social welfare system; it is also a model from which nations can develop their national priority assistive products lists. The aim of this opinion paper is to share my views about the Priority Assistive Products List on the grounds that it makes no distinct mention of sexual assistive devices, yet research has indicated that sexuality is an area of great concern for persons with disabilities. In any case, sexuality forms a core part of being human, and it impacts on both the physical and mental well-being of all human beings. I conclude in part that, in its present format, the list perpetuates the myth that persons with disabilities are asexual beings who are innocent of sexual thoughts, feelings and experiences. The list also propagates the stereotype that sexuality is a sacred, private, bedroom matter that should be kept out of the public domain, to the detriment of the health and well-being of persons with disabilities.

scholarly journals A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Gestational Diabetes Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1001-1005
Author(s):  
Lokesh Kola
Keyword(s):  
Machine Learning ◽  
Gestational Diabetes ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Middle Income ◽  
Diabetes Prediction ◽  
Supervised Machine Learning Classifiers ◽  
Health Organization

Abstract: Diabetes is the deadliest chronic diseases in the world. According to World Health Organization (WHO) around 422 million people are currently suffering from diabetes, particularly in low and middle-income countries. Also, the number of deaths due to diabetes is close to 1.6 million. Recent research has proven that the occurrence of diabetes is likely to be seen in people aged between 18 and this has risen from 4.7 to 8.5% from 1980 to 2014. Early diagnosis is necessary so that the disease does not go into advanced stages which is quite difficult to cure. Significant research has been performed in diabetes predictions. As time passes, challenges keep increasing to build a system to detect diabetes systematically. The hype for Machine Learning is increasing day to day to analyse medical data to diagnose a disease. Previous research has focused on just identifying the diabetes without specifying its type. In this paper, we have we have predicted gestational diabetes (Type-3) by comparing various supervised and semi-supervised machine learning algorithms on two datasets i.e., binned and non-binned datasets and compared the performance based on evaluation metrics. Keywords: Gestational diabetes, Machine Learning, Supervised Learning, Semi-Supervised Learning, Diabetes Prediction

scholarly journals COVID-19 in the International Year of Plant Health

2022 ◽  
Vol 39 (4) ◽  
Author(s):  
José Jorge Gutiérrez-Samperio
Keyword(s):  
Life Forms ◽  
Plant Health ◽  
World Health ◽  
Human Beings ◽  
Pests And Diseases ◽  
The Bible ◽  
United Nations General ◽  
The World ◽  
The Face ◽  
Health Organization

<p>Pests, in their broad sense, have played an important part in the history of humankind. We could say that humans, crops and pests have walked together through life. Codices, glyphs, paintings and countless ancient documents, including the Bible and the Koran, bear witness to this. Humanity has been attacked by its own diseases, but also by those that limit them from obtaining food and deteriorate the environment. COVID-19, which is now troubling us and was declared a pandemic by the World Health Organization in March of 2020, became a part of the list of experiences we have suffered in the past, with pests or epidemics that caused millions of deaths by diseases or famines. It is paradoxical that this health contingency occurs when the United Nations General Assembly, on December 20th, 2018, in its resolution A/RES/73/252 decides to declare 2020 the International Year of Plant Health in order to “highlight the importance of plant health to improve food security, protect the environment and biodiversity and boost economic development” according to the pronouncement by the FAO. For the first time, in an era with great technological and scientific breakthroughs, humanity was aware of its vulnerability against the inevitable evolution of life forms in the face of dilemmas global impact caused by human beings. Thus, the pest or parasite makes its own declaration of existential preeminence through SARS-CoV-2 to remind us that the health of humans or plants is the essence of life and its continuity. But perhaps absolute health is not enough. It is necessary to find a balance in a world overwhelmed by giving so much in return for almost nothing to everyone living on it. If the sensor of our anthropocentric intervention of the world is climate change, then biological chaos is a masterpiece. The reemergence of pests and diseases considered eradicated, or those of zoonotic origin that had never accompanied our existence is a surreal dystopia that we will never be able to deny again.</p>

scholarly journals An evaluation of emergency guidelines issued by the World Health Organization in response to four infectious disease outbreaks

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0198125 ◽  
Author(s):  
Susan L. Norris ◽  
Veronica Ivey Sawin ◽  
Mauricio Ferri ◽  
Laura Raques Sastre ◽  
Teegwendé V. Porgo
Keyword(s):  
Infectious Disease ◽  
World Health Organization ◽  
Disease Outbreaks ◽  
World Health ◽  
Infectious Disease Outbreaks ◽  
The World ◽  
Health Organization

scholarly journals SARS: Political Pathology of the First Post-Westphalian Pathogen

2003 ◽  
Vol 31 (4) ◽  
pp. 485-505 ◽  
Author(s):  
David P. Fidler
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
The United States ◽  
First Century ◽  
World Health ◽  
Health Security ◽  
The World ◽  
Twenty First Century ◽  
The Stability ◽  
Health Organization

In March 2003, the world discovered, again, that I humanity's battle with infectious diseases continues. The twenty-first century began with infectious diseases, especially HIV/AIDS, being discussed as threats to human rights, economic development, and national security. Bioterrorism in the United States in October 2001 increased concerns about pathogenic microbes. The global outbreak of severe acute respiratory syndrome (SARS) in the spring of 2003 kept the global infectious disease challenge at the forefront of world news for weeks. At its May 2003 annual meeting, the World Health organization (WHO) asserted that SARS is “the first severe infectious disease to emerge in the twenty-first century” and “poses a serious threat to global health security, the livelihood of populations, the functioning of health systems, and the stability and growth of economies.”

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