scholarly journals Forecasting under applying machine learning and statistical models

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 131-137
Author(s):  
Azhari Elhag ◽  
Hanaa Abu-Zinadah
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Real Life ◽  
World Health ◽  
Percentage Error ◽  
Learning Models ◽  
Kingdom Of Saudi Arabia ◽  
The World ◽  
Data Source ◽  
Health Organization

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.

scholarly journals Forecasting under applying machine learning and statistical models

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 131-137
Author(s):  
Azhari Elhag ◽  
Hanaa Abu-Zinadah
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Real Life ◽  
World Health ◽  
Percentage Error ◽  
Learning Models ◽  
Kingdom Of Saudi Arabia ◽  
The World ◽  
Data Source ◽  
Health Organization

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.

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.

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 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 Epidemiology of COVID-19: What have we learnt until now?

2021 ◽  
Vol 72 (3) ◽  
pp. 8-11
Author(s):  
Tatjana Pekmezović
Keyword(s):  
Herd Immunity ◽  
Real Life ◽  
Rapid Expansion ◽  
World Health ◽  
Unknown Etiology ◽  
Atypical Pneumonia ◽  
First Case ◽  
The World ◽  
Severity Of The Disease ◽  
Health Organization

The first case in the outbreak of atypical pneumonia of unknown etiology, later confirmed as disease caused by SARS-CoV-2, was described in Wuhan (China) on December 8, 2019. The rapid expansion of COVID-19 cases prompted the World Health Organization (WHO) to declare a global health emergency, and on March 11, 2020, COVID-19 was officially classified as a pandemic disease by the WHO. It is generally accepted that both genders and all ages in the population are susceptible to SARS-CoV-2 infection. Data from the real life also show difficulties in reaching the threshold of herd immunity. Thanks to the vaccination, some populations are approaching the theoretical threshold of immunity, but the spread of the virus is still difficult to stop. If we add to that the fact that we still do not know how long immunity lasts after the infection, the conclusion is that vaccination is unlikely to completely stop the spread of the virus, and that we must think about it. Vaccines certainly significantly reduce the hospitalization rate and mortality rate, and the assumption is that the virus will not disappear soon, but the severity of the disease and its fatality will be of marginal importance. The development of the epidemiological situation related to the COVID-19 is constantly changing and it significantly differs in various parts of the world, which is affected by differences in financial resources, health infrastructure and awareness of prevention and control of the COVID-19. Attempts are being made to make dynamically adjusted strategies in response to the COVID-19 pandemic, that is, the new normality.

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.

Developing a Medical Ontology for Diabetes Treatment

2019 ◽  
Vol 16 (9) ◽  
pp. 3783-3791
Author(s):  
Hala Alrumaih ◽  
Mohammed Alawairdhi
Keyword(s):  
Saudi Arabia ◽  
International Organizations ◽  
World Health ◽  
Diabetes Treatment ◽  
Kingdom Of Saudi Arabia ◽  
Medical Issues ◽  
The World ◽  
Diabetes Center ◽  
Health Organization ◽  
The University

A great wave of diabetes is sweeping the world and it does not exclude continent, country or society and this has led international organizations such as the World Health Organization to sound the alarm after the high rates of deaths from complications of this epidemic. In this regard, the University Diabetes Center (UDC) was established in the Kingdom of Saudi Arabia to provide medical care for people with diabetes among other medical issues. As part of UDC, an ontology center has been constructed to explain the domain of the UDC using Protégé. The discussion herein will center on how emerging diabetes centers can benefit from the UDC experiment in diabetes treatment.

scholarly journals Predicting the Pandemic COVID-19 Using ARIMA Model

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Nguyen Quoc Duong ◽  
Le Phuong Thao ◽  
Dinh Thi Nhu Quynh ◽  
Le Thanh Binh ◽  
Cao Thi Ai Loan ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Percentage Error ◽  
The World ◽  
Validation Set ◽  
Short Term Forecasting ◽  
Health Organization ◽  
Global Threat

Coronavirus disease 2019 (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. The main objective of this study is to apply AutoRegressive Integrated Moving Average (ARIMA) model with the objective of monitoring and short-term forecasting the total confirmed new cases per day all over the world. The data are extracted from daily report of World Health Organization from 21st January 2020 to 16th March 2020. Akaike’s Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. To assess the validity of the proposed model, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) between the observed and fitted of COVID-19 total confirmed new cases was calculated. Finally, we applied “forecast” package in R software and the fitted ARIMA model to predict the infections of COVID-19. We found that the ARIMA (1, 2, 1) model was able to describe and predict the epidemiological trend of the disease of COVID-19. The MAPE and RMSE for the training set and validation set respectively, which we found was reasonable for use in the forecast. Furthermore, the model also provided forecast total confirmed new cases for the following days. ARIMA model applied to COVID-19 confirmed cases data are an important tool for COVID-19 surveillance all over the world. This study shows that accurate forecasting of the COVID-19 trend is possible using an ARIMA model. Unless strict infection management and control are taken, our findings indicate the potential of COVID-19 to cause greater outbreak all over the world.

scholarly journals Time-Series prediction for the epidemic trends of COVID-19 using Conditional Generative adversarial Networks Regression on country-wise case studies

2021 ◽  
Author(s):  
Arnabi Bej ◽  
Ujjwal Maulik ◽  
Anasua Sarkar
Keyword(s):  
Machine Learning ◽  
Time Series Prediction ◽  
Statistical Technique ◽  
World Health ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Wide Range ◽  
The World ◽  
Virulent Virus ◽  
Health Organization

Abstract Probabilistic Regression is a statistical technique and a crucial problem in the machine learning domain which employs a set of machine learning methods to forecast a continuous target variable based on the value of one or multiple predictor variables. COVID-19 is a virulent virus that has brought the whole world to a standstill. The potential of the virus to cause inter human transmission makes the world a dangerous place. This thesis predicts the upcoming circumstances of the Corona virus to subside its action. We have performed Conditional GAN regression to anticipate the subsequent Covid-19 cases of 5 countries. The GAN variant CGAN is used to design the model and predict the Covid-19 cases for three months ahead with least error for the dataset provided. Each country is examined individually, due to their variation in population size, tradition, medical manage- ment, preventive measures. The analysis is based on confirmed data, as provided by the World Health Organization. This paper investigates how conditional Generative Adversarial Networks (GANs) can be used to accurately exhibit intricate conditional distributions. GANs have got spectacular achievement in producing convoluted highdimensional data, but work done on their use for regression prob- lems is minimal. This paper exhibits how conditional GANs can be employed in probabilistic regression. It is shown that conditional GANs can be used to evaluate a wide range of various distributions and be competitive with existing probabilistic regression models.

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