scholarly journals AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

2021 ◽  
Author(s):  
Naser Zaeri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ct Scan ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Related Data ◽  
Recent Developments ◽  
Intelligence Modeling ◽  
Health Organization ◽  
Imaging Algorithms

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

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

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 A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey

2020 ◽  
Author(s):  
Fareeha Anwar ◽  
Qurat-Ul-Ain ◽  
Muhammad Yasir Ejaz ◽  
Amir Mosavi
Keyword(s):  
Artificial Intelligence ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Heart Rate ◽  
Deep Learning ◽  
Hybrid Methods ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Limb Amputation ◽  
Diabetes Diagnosis

Diabetes Mellitus is commonly known as diabetes. It is one of the most chronic diseases as the World Health Organization (WHO) report shows that the number of diabetes patients has risen from 108 million to 422 million in 2014. Early diagnosis of diabetes is important because it can cause different diseases that include kidney failure, stroke, blindness, heart attacks, and lower limb amputation. Different diabetes diagnosis models are found in literature, but there is still a need to perform a survey to analyze which model is best. This paper performs a literature review for diabetes diagnosis approaches using Artificial Intelligence (neural networks, machine learning, deep learning, hybrid methods, and/or stacked-integrated use of different machine learning algorithms). More than thirty-five papers have been shortlisted that focus on diabetes diagnosis approaches. Different datasets are available online for the diagnosis of diabetes. Pima Indian Diabetes Dataset (PIDD) is the most commonly used for diabetes prediction. In contrast with other datasets, it has key factors which play an important role in diabetes diagnosis. This survey also throws light on the weaknesses of the existing approaches that make them less appropriate for a diabetes diagnosis. In artificial intelligence techniques, deep learning is widespread and in medical research, heart rate is getting more attention. Deep learning combined with other algorithms can give better results in diabetes diagnosis and heart rate should be used for other cardiac disease diagnoses.

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 Mapping the landscape of Artificial Intelligence applications against COVID-19

2020 ◽  
Vol 69 ◽  
pp. 807-845 ◽  
Author(s):  
Joseph Bullock ◽  
Alexandra Luccioni ◽  
Katherine Hoffman Pham ◽  
Cynthia Sin Nga Lam ◽  
Miguel Luengo-Oroz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Open Science ◽  
Data Sources ◽  
World Health ◽  
Artificial Intelligence Research ◽  
Non Invasive ◽  
Multiple Data ◽  
The World ◽  
Health Organization

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

scholarly journals Stroke Disease Detection and Prediction Using Robust Learning Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tahia Tazin ◽  
Md Nur Alam ◽  
Nahian Nakiba Dola ◽  
Mohammad Sajibul Bari ◽  
Sami Bourouis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Recognition ◽  
Warning Signs ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Approaches ◽  
Numerous Model ◽  
Health Organization ◽  
The Brain

Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction. Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable. Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis.

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.

scholarly journals An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 176
Author(s):  
A Lakshmanarao ◽  
M Raja Babu ◽  
T Srinivasa Ravi Kiran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Polynomial Regression ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Main Concern ◽  
Python Language ◽  
Precautionary Measures ◽  
Health Organization

<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>

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 Cloud Based Predictive Model for Airborne Disease Based Healthcare Data

2019 ◽  
Vol 9 (2) ◽  
pp. 837-840
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Predictive Model ◽  
Health Impact ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Attention ◽  
Healthcare Data ◽  
Health Organization ◽  
Algorithmic Approaches

Nowadays, the airborne particles have major health impact when it spreads in human, plant and animal beings. Infectious diseases spreads from these particles which are exhaled directly into the air through the exertions of coughing, breathing, talking and sneezing etc. According to the report from World Health Organization (WHO), More than 30 infectious diseases have arrived to harm the health of people in the past years. There’s no medical attention for several infectious diseases to take prevention and remedy. India have lack of healthcare data to take control of the endemic infectious diseases. This paper uses predictive model which is provide a preventive guidance and suggestions for predicted Airborne diseases through machine learning algorithms. Azure machine learning studio is a cloud based environment which provides machine learning algorithmic approaches to make an intelligent model based solution to solve the particular domain based problems. This proposed model will produce an efficient outcome and helps to take better protection from the infectious diseases.

Sign in / Sign up

Export Citation Format

Share Document