scholarly journals Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis

2020 ◽  
Vol 14 (suppl 1) ◽  
pp. 1017-1024 ◽  
Author(s):  
Mohammad Khubeb Siddiqui ◽  
Ruben Morales-Menendez ◽  
Pradeep Kumar Gupta ◽  
Hafiz M.N. Iqbal ◽  
Fida Hussain ◽  
...  
Keyword(s):  
Machine Learning ◽  
World Health ◽  
Effect Of Temperature ◽  
Machine Learning Method ◽  
Contributing Factors ◽  
Data Set ◽  
The World ◽  
Health Organization ◽  
Death Cases ◽  
Learning Analysis

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

ARIMA model for forecasting COVID-19 Death cases in India (Preprint)

2020 ◽  
Author(s):  
Jeya Sutha M
Keyword(s):  
Arima Model ◽  
Public Health Emergency ◽  
World Health ◽  
The People ◽  
The World ◽  
The Status ◽  
The Government ◽  
Health Organization ◽  
Spread Of Infection ◽  
Death Cases

UNSTRUCTURED COVID-19, the disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious disease. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of July 25, 2020; 15,947,292 laboratory-confirmed and 642,814 deaths have been reported globally. India has reported 1,338,928 confirmed cases and 31,412 deaths till date. This paper presents different aspects of COVID-19, visualization of the spread of infection and presents the ARIMA model for forecasting the status of COVID-19 death cases in the next 50 days in order to take necessary precaution by the Government to save the people.

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

2021 ◽  
Author(s):  
◽  
Zayra Ramírez Gaytán
Keyword(s):  
World Health ◽  
Degenerative Diseases ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
The World ◽  
Long Time ◽  
Life Threatening ◽  
Health Organization ◽  
Logistic Regression Algorithm ◽  
Asymptomatic Phase

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases. According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms: logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

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.

scholarly journals Compliance of clinical trial registries with the World Health Organization minimum data set: a survey

Trials ◽  
2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Lorenzo P Moja ◽  
Ivan Moschetti ◽  
Munira Nurbhai ◽  
Anna Compagnoni ◽  
Alessandro Liberati ◽  
...  
Keyword(s):  
Clinical Trial ◽  
World Health Organization ◽  
World Health ◽  
Minimum Data Set ◽  
Data Set ◽  
Clinical Trial Registries ◽  
Minimum Data ◽  
The World ◽  
Health Organization

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

2021 ◽  
Vol 17 (08) ◽  
pp. 154
Author(s):  
Shakir Khan
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Prediction Models ◽  
Preventive Measures ◽  
Future Trend ◽  
Control Measures ◽  
World Health ◽  
Seir Model ◽  
The World ◽  
Health Organization

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

scholarly journals Update on the SARS-CoV-2 (COVID-19) Outbreak: A Global Pandemic Challenge

2020 ◽  
Author(s):  
Abhishek Kumar Soni
Keyword(s):  
World Health ◽  
Global Public Health ◽  
Global Challenge ◽  
Global Pandemic ◽  
Therapeutic Algorithms ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization ◽  
And Control ◽  
Death Cases

The 2019 novel coronavirus (previously 2019-nCoV) or coronavirus infectious disease 2019 (COVID-19) outbreak has been summarized as on March 29, 2020. COVID-19 is a highly transmittable and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SERS-CoV-2). The disease was first seen during an outbreak in Wuhan, China and continuous spreading from human to human around the sphere. The disease is uncontrolled and increasing the death toll through. The world is facing a global challenge to protect human lives caused by coronavirus outbreak. The number of infected patients is increasing day by day due to COVID-19 as a pandemic. The world health organization (WHO) has declared global public health emergency on January 30, 2020. The disease has been spread around 201 countries with total confirmed cases 634835 and death cases 29891 as on March 29, 2020. The goal of this review to summaries and update the clinical/medical features and suggestions for diagnosis of the COVID-19 as a pandemic. The discussion of the various therapeutic algorithms, risk, prevention and control based on the latest reports has been provided.

scholarly journals The Infection of COVID-19 among Health Care Workers in Dharmais Cancer Hospital

2021 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Mutiara Adelina ◽  
Fifi Dwijayanti
Keyword(s):  
Health Care ◽  
Health Care Workers ◽  
World Health ◽  
Care Workers ◽  
The World ◽  
Risk Condition ◽  
Contact Transmission ◽  
Health Organization ◽  
And Control ◽  
Death Cases

Infectious diseases are one of the biggest threats to humans. Currently, the world is in the outbreak condition causes of the COVID-19 virus which is started from Wuhan, China in December 2019. This disease was spread out rapidly throughout the World and was announced as a pandemic by the World Health Organization (WHO) on March 11, 2020(1). The infected number of SARS-CoV-2 was over 84 million people and caused over 1 million death cases in the worldwide. Indonesia had more than 800.000 infectious cases and 23.000 of death cases with the highest cases in Jakarta (2). This virus can be transmitted by two ways, such as direct contact (cough, sneeze, and droplet inhalation) and contact transmission (contact with oral, nasal, and eye mucous membranes) of person with COVID-19 (3). The current COVID-19 pandemic makes various challenges in prevention and control of infections in hospitals. Health care workers (HCWs) have been providing care to suspected, probable or confirmed COVID-19 patients that make them in high-risk condition. Several study indicated that many HCWs have been infected with SARS-CoV-2 in many hospitals worldwide (4)(5)(6).

scholarly journals Predictive Modeling Of Diabetic Retinopathy Disease By Use Of Machine Learning

2021 ◽  
Vol 23 (12) ◽  
pp. 423-430
Author(s):  
Sandeep Prakash ◽  
◽  
Dr Pankaj Prajapati ◽  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Causes Of Death ◽  
World Health ◽  
Machine Learning Method ◽  
Diabetes Retinopathy ◽  
Uncontrolled Diabetes ◽  
Serious Disease ◽  
Health Organization ◽  
Timely Treatment

According to a report by the World Health Organization (WHO), one of the leading causes of death by the end of 2030 will be diabetes, which is a serious disease. Timely treatment of this disease can prevent serious complications, including death. The number of people getting infected with diabetes is millions. The risk of getting this infection is common now a days and is more prevalent in women than men. Diagnosis process for diabetes is quite tedious. Diabetes retinopathy is a disorder that is caused by uncontrolled diabetes and can cause complete blindness if left untreated. Therefore, if detected early its treatment can prevent the unfavourable effects of diabetic retinopathy. The actual diagnosis of diabetes retinopathy by eye doctors takes a lot of time and patients need to suffer more during this time. Thus the latest achievements in science and technology makes it easier to predict the disease. The aim is to diagnose whether a person is diabetic or not using a phase-based machine learning method. This paper reviews, classifies and compares algorithms with previously suggested strategies to develop better and more efficient algorithms.

scholarly journals An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Tian-Ao Xie ◽  
Zhi-Jian He ◽  
Chuan Liang ◽  
Hao-Neng Dong ◽  
Jie Zhou ◽  
...  
Keyword(s):  
World Health ◽  
Pathophysiological Mechanism ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Online Tool ◽  
Protein Protein Interaction ◽  
The World ◽  
Health Organization ◽  
Gene Data

Abstract Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. Methods The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein–protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. Results A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. Conclusion The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

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