scholarly journals Covid-19 Forecasting using Supervised Machine Learning Techniques – Survey

2021 ◽  
Vol 309 ◽  
pp. 01218
Author(s):  
P. Lakshmi Sruthi ◽  
K. Butchi Raju
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Big Data Analytics ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
World Health ◽  
Global Epidemic ◽  
Effective Strategies ◽  
Health Organization ◽  
National Databases

COVID-19 is a global epidemic that has spread to over 170 nations. In practically all of the countries affected, the number of infected and death cases has been rising rapidly. Forecasting approaches can be implemented, resulting in the development of more effective strategies and the making of more informed judgments. These strategies examine historical data in order to make more accurate predictions about what will happen in the future. These forecasts could aid in preparing for potential risks and consequences. In order to create accurate findings, forecasting techniques are crucial. Forecasting strategies based on Big data analytics acquired from National databases (or) World Health Organization, as well as machine learning (or) data science techniques are classified in this study. This study shows the ability to predict the number of cases affected by COVID-19 as potential risk to mankind.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

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 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 Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions (Preprint)

2018 ◽  
Author(s):  
Roberto Acuña
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Machine Learning Techniques ◽  
World Health ◽  
Global Public Health ◽  
Systematic Analysis ◽  
Middle Income ◽  
Learning Techniques ◽  
Health Organization

BACKGROUND According to the World Health Organization (WHO) close to 800,000 people worldwide death by suicidal each year. Many more attempt to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries, but poor and middle income countries as well. OBJECTIVE The aim of this study is to evaluate several supervised classifiers for detecting messages with suicidal ideation in order to know if these systems can be used in automatic suicide prevention systems. METHODS We used machine learning techniques to make a systematic analysis of 28 supervised classifier algorithms with parameters by defect. The Life Corpus, used in this research, is a bilingual corpus (English and Spanish) oriented to suicide. The corpus was constructed by two annotation experts, retrieving texts from several social networks. The corpus quality was measured using mutual annotation agreement. RESULTS The different experiments determined that the classifier with the best performance was KStar, with the corpus version POS-SYNSETS-NUM; and the cycle with 2 classes Urgent and No Risk was the one that achieved the best results with the PRC-Area metrics of 0,81036 and F-measure of 0,7148. CONCLUSIONS The present research fulfilled the objective of discovering which characteristics are the most suitable for the automatic classification of messages with suicidal ideation, using the Life Corpus. The results of this evaluation demonstrate that the Life Corpus and machine learning techniques could be suitable for detecting suicide ideation messages.

scholarly journals Exploring the Efficiency of Various Supervised Machine Learning Techniques to Predict the Heart Disease using Risk Factors

2019 ◽  
Vol 9 (1S) ◽  
pp. 309-312
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Heart Disease ◽  
Major Part ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set

Data Science in healthcare is a innovative and capable for industry implementing the data science applications. Data analytics is recent science in to discover the medical data set to explore and discover the disease. It’s a beginning attempt to identify the disease with the help of large amount of medical dataset. Using this data science methodology, it makes the user to find their disease without the help of health care centres. Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Health care information is very effective in the society. In a human life day to day heart disease had increased. Based on the heart disease to monitor different factors in human body to analyse and prevent the heart disease. To classify the factors using the machine learning algorithms and to predict the disease is major part. Major part of involves machine level based supervised learning algorithm such as SVM, Naviebayes, Decision Trees and Random forest.

scholarly journals An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 750-752
Keyword(s):  
Machine Learning ◽  
Similarity Measures ◽  
Machine Learning Techniques ◽  
World Health ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Student Dropout ◽  
Learning Techniques ◽  
Using Data ◽  
Health Organization

Education could be a important resource that has to lean to all or any kids. one in all the largest assets of the longer term generation cloud is alleged because the education that's given to the youngsters. Most of the youngsters aren't ready to continue their education because of many reasons. The prediction of student dropout plays a very important role in characteristic the scholars World Health Organization are on the sting of being a dropout from their education. whereas predicting this, we will simply try and solve their issues and create them continue their education. during this paper, we've planned a model for predicting the scholars can get born out or not mistreatment many machine learning techniques. we have a tendency to create use of decision trees that make a call mistreatment many factors. the choice of the prediction involves crucial wherever many knowledge attributes are used for prediction like correlations, similarity measures, frequent patterns, and associations rule mining. The planned work is evaluated mistreatment numerous parameters and is well-tried to figure expeditiously in predicting the dropout students compared with alternative.

scholarly journals Information Retrieval using Machine Learning for Biomarker Curation in the Exposome-Explorer

2020 ◽  
Author(s):  
Andre Lamurias ◽  
Sofia Jesus ◽  
Vanessa Neveu ◽  
Reza M Salek ◽  
Francisco M Couto
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
World Health ◽  
International Agency ◽  
Scientific Publications ◽  
Manual Curation ◽  
Similar Chemical ◽  
Domain Expertise ◽  
Health Organization ◽  
Logistic Regression Algorithm

AbstractIn 2016, the International Agency for Research on Cancer, part of the World Health Organization, released the Exposome-Explorer, the first database dedicated to biomarkers of exposure for environmental risk factors for diseases. The database contents resulted from a manual literature search that yielded over 8500 citations, but only a small fraction of these publications were used in the final database. Manually curating a database is time-consuming and requires domain expertise to gather relevant data scattered throughout millions of articles. This work proposes a supervised machine learning approach to assist the previous manual literature retrieval process.The manually retrieved corpus of scientific publications used in the Exposome-Explorer was used as training and testing sets for the machine learning models (classifiers). Several parameters and algorithms were evaluated to predict an article’s relevance based on different datasets made of titles, abstracts and metadata.The top performance classifier was built with the Logistic Regression algorithm using the title and abstract set, achieving an F2-score of 70.1%. Furthermore, from 705 articles classified as relevant, we extracted 545 biomarkers, including 460 new candidate entries to the Exposome-Explorer database.Our methodology reduced the number of articles to be manually screened by the database curators by nearly 90%, while only misclassifying 22.1% of the relevant articles. We expect that this methodology can also be applied to similar biomarkers datasets or be adapted to assist the manual curation process of similar chemical or disease databases.

scholarly journals Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

2022 ◽  
Author(s):  
Renáta Németh ◽  
Fanni Máté ◽  
Eszter Katona ◽  
Márton Rakovics ◽  
Domonkos Sik
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Data Science ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Online Health Communities ◽  
Health Communities ◽  
Open Question

AbstractSupervised machine learning on textual data has successful industrial/business applications, but it is an open question whether it can be utilized in social knowledge building outside the scope of hermeneutically more trivial cases. Combining sociology and data science raises several methodological and epistemological questions. In our study the discursive framing of depression is explored in online health communities. Three discursive frameworks are introduced: the bio-medical, psychological, and social framings of depression. ~80 000 posts were collected, and a sample of them was manually classified. Conventional bag-of-words models, Gradient Boosting Machine, word-embedding-based models and a state-of-the-art Transformer-based model with transfer learning, called DistilBERT were applied to expand this classification on the whole database. According to our experience ‘discursive framing’ proves to be a complex and hermeneutically difficult concept, which affects the degree of both inter-annotator agreement and predictive performance. Our finding confirms that the level of inter-annotator disagreement provides a good estimate for the objective difficulty of the classification. By identifying the most important terms, we also interpreted the classification algorithms, which is of great importance in social sciences. We are convinced that machine learning techniques can extend the horizon of qualitative text analysis. Our paper supports a smooth fit of the new techniques into the traditional toolbox of social sciences.

Advocacy in dementia

2019 ◽  
pp. 291-302
Author(s):  
Gorazd B. Stokin
Keyword(s):  
Cost Effective ◽  
Dementia Care ◽  
World Health ◽  
Political Systems ◽  
Media Campaigns ◽  
Middle Income ◽  
Global Epidemic ◽  
Effective Strategies ◽  
Middle Income Countries ◽  
Health Organization

Advocacy in dementia can be defined best as the act or process by an individual or a group influencing or otherwise supporting within social, health, economic, and political systems and organizations better dementia care at large. Dementia advocacy encompasses many activities including among others public speaking and media campaigns, sharing knowledge and experiences, providing resources including funding, establishing groups and organizations, developing and presenting guidelines, criteria, programmes, strategies, and policies and consulting regional, national, and international decision-makers to promote, support, and otherwise further dementia care. Recently, the World Health Organization recognized dementia as a global epidemic with the majority of people afflicted by dementia originating from low- to middle-income countries where access to dementia care is limited or absent. Indeed, there is an urgent need to develop cost-effective strategies to deliver sufficient and efficient dementia care as well as to optimize needed resources including finances. This need can only be fulfilled with diligent advocacy, which initially played a crucial role in defining the modern notion of dementia and more recently propelled dementia to the centre stage of healthcare priorities across the globe.

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