scholarly journals Machine Learning Model and Statistical Methods for COVID-19 Evolution Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
M. D. Alsulami ◽  
Hanaa Abu-Zinadah ◽  
Anwar Hassan Ibrahim
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Statistical Processing ◽  
Economic Consequences ◽  
World Health ◽  
Population Densities ◽  
Tree Model ◽  
The United Kingdom ◽  
Machine Learning Model ◽  
The World

In this paper, we discuss the statistical processing of COVID-19 data. COVID-19 was initially recognized in Wuhan, China, on December 31, 2019. It then spread to other parts of the world, so it became known as a pandemic. It has received interest due to its sudden emergence as a deadly human pathogen. The effect is not only confined to morbidity and mortality but also extends to social and economic consequences. Statistical analysis is required to measure the damage done to humans and take the necessary measures to limit this damage. The objective of the work was to examine the effects of various factors on the deaths due to COVID-19. To achieve this goal, we applied a logistic regression (LR) model, as a statistical method, and a decision tree model, as a machine learning method, to model the deaths due to COVID-19 in France, Germany, Italy, and Spain. The predictive abilities of these two models were compared. The overall accuracies of the decision tree and LR were 94.1% and 93.9%, respectively. It was also observed that countries with high population densities tended to have more cases than those with smaller population densities. There were more female deaths than male deaths in the United Kingdom, and more deaths occurred for those aged 65 years and older. The data were collected from the World Health Organization’s official website from January 11, 2020, to May 29, 2020. The results obtained were in agreement with the previous results obtained by others.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals treeheatr: an R package for interpretable decision tree visualizations

2020 ◽  
Author(s):  
Trang T. Le ◽  
Jason H. Moore
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Feature Space ◽  
R Package ◽  
Tree Structure ◽  
Decision Tree Model ◽  
Teaching Tool ◽  
Tree Model ◽  
Machine Learning Methods ◽  
Link Type

AbstractSummarytreeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods.AvailabilityThe treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous [email protected]

scholarly journals Communication Sentiment Analyzer using Machine Learning with Naive Bayes Bernoullinb

2019 ◽  
Vol 9 (1) ◽  
pp. 5976-5979
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Major Part ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Social Media Data ◽  
Machine Learning Model ◽  
The World ◽  
Media Data

In this never-ending social media era it is estimated that over 5 billion people use smartphones. Out of these, there are over 1.5 billion active users in the world. In which we all are a major part and before opening our messages we all are curious about what message we have received. No doubt, we all always hope for a good message to be received. So Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Finally, we propose a scalable machine learning model to analyze the polarity of a communicative text using Naive Bayes’ Bernoulli classifier. This paper works on only two polarities that is whether the sentence is positive or negative. Bernoulli classifier is used in this paper because it is best suited for binary inputs which in turn enhances the accuracy of up to 97%.

scholarly journals GENRE PREDICTION FOR MUSIC RECOMMENDATION USING MACHINE LEARNING

2020 ◽  
pp. 206-210
Author(s):  
Arpit Seth
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Music Recommendation ◽  
Age And Gender ◽  
Vast Number ◽  
The World ◽  
And Gender

Music applications are one of the most used applications in the world. Consumers can hear the song they like but difficult for them to find songs from the vast number of songs list. The flow of this paper is to increase the efficiency of music recommendation in terms of the genre based on the decision-tree which helps the users to get the music according to their preferences. This model uses age and gender as an input set and genre as output. The model will predict the genre according to age and gender and the decision tree helps to reduce the complexity of the model.

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.

Whose Territory is it Anyway? Should Psychiatry own Alcohol and Drugs in Australia?

1994 ◽  
Vol 28 (3) ◽  
pp. 375-377 ◽  
Author(s):  
Alex Wodak
Keyword(s):  
World Health Organisation ◽  
The United States ◽  
World Health ◽  
Improve Treatment ◽  
The United Kingdom ◽  
Alcohol And Drugs ◽  
The World ◽  
The Subject ◽  
The Many ◽  
Drug Problems

Surely alcohol and drug matters in Australia should be regarded as the province of psychiatry? Decades before any other branch of medicine displayed any interest in the subject and long before alcohol and drugs were considered even remotely respectable, numerous Australian psychiatrists provided inspiration and leadership in this Cinderella field. Drs Bartholomew, Bell, Buchanan, Chegwidden, Dalton, Drew, Ellard, Lennane, Milner, Milton, Waddy and Pols are some of the best known among the many Australian psychiatrists who pioneered efforts to improve treatment for patients with alcohol and drug problems. The NHMRC Committee on Alcohol and Drug Dependence, which has a considerable potential for influencing the field in Australia, has always been dominated by psychiatrists. In the United Kingdom and the United States, countries which often serve as models for much of Australian medical and other practice, alcohol and drug matters are determined almost exclusively by psychiatrists. Is there any evidence that they have been held back by a psychiatric hegemony on alcohol and drug's? For many decades (and until quite recently), alcohol and drug matters were handled for the World Health Organisation by its Mental Health Division. Did we suffer globally because WHO placed alcohol and drugs under the control of psychiatry?

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 How did the UK government face the global COVID-19 pandemic?

2021 ◽  
Vol 55 (1) ◽  
pp. 72-83 ◽  
Author(s):  
Tamiris Cristhina Resende ◽  
Marco Antonio Catussi Paschoalotto ◽  
Stephen Peckham ◽  
Claudia Souza Passador ◽  
João Luiz Passador
Keyword(s):  
United Kingdom ◽  
Action Plan ◽  
World Health Organisation ◽  
World Health ◽  
Prevention Measures ◽  
The United Kingdom ◽  
First Case ◽  
The World ◽  
Uk Government ◽  
The Uk

Abstract This paper aims to analyse the coordination and cooperation in Primary Health Care (PHC) measures adopted by the British government against the spread of the COVID-19. PHC is clearly part of the solution founded by governments across the world to fight against the spread of the virus. Data analysis was performed based on coordination, cooperation, and PHC literature crossed with documentary analysis of the situation reports released by the World Health Organisation and documents, guides, speeches and action plans on the official UK government website. The measures adopted by the United Kingdom were analysed in four periods, which helps to explain the courses of action during the pandemic: pre-first case (January 22- January 31, 2020), developing prevention measures (February 1 -February 29, 2020), first Action Plan (March 1- March 23, 2020) and lockdown (March 24-May 6, 2020). Despite the lack of consensus in essential matters such as Brexit, the nations in the United Kingdom are working together with a high level of cooperation and coordination in decision-making during the COVID-19 pandemic.

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 Application of decision tree model for prediction of immigration policy in different countries of the world

2021 ◽  
Vol 19 (3) ◽  
pp. 513-532
Author(s):  
Viktoriia Apalkova ◽  
Sergiy Tsyganov ◽  
Nataliia Meshko ◽  
Nadiia Tsyganova ◽  
Serhii Apalkov
Keyword(s):  
Decision Tree ◽  
Immigration Policy ◽  
Environmental Performance ◽  
Ecological Factors ◽  
Environmental Indicators ◽  
Immigration Policies ◽  
Skilled Workers ◽  
Tree Model ◽  
Community Interest ◽  
The World

In the past few decades, the ever-increasing dynamics of international migration flows can be observed. At this stage, the governments of major countries in the world are striving to balance the needs of their citizens and the support of immigrants. The paper analyzes factors that affect the immigration policies of various countries and determines the role of ecological factors (such as environmental conditions). The objective of the study is to predict the immigration policies of different countries of the world based on the analysis of the influencing factors, including environmental performance. The research method is based on the use of the RapidMiner software package to build two decision tree models and a static index database of more than 150 countries around the world. The results show that in most cases, the immigration policies of various countries will focus on maintaining the current level of immigration and increasing the number of skilled workers. At the same time, one of the key decision-making factors will be the country’s current immigration level, environmental performance, GDP per capita, and the Education index. One of the main conclusions is that the country’s environmental indicators have begun to become one of the priority values that determine the state immigration policy. This can be explained by the rising global community interest in the challenges of climate change.

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