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 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.

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.

The impact of SARS-Cov-2 on the Nervous system and Mental Health

2021 ◽  
Vol 19 ◽  
Author(s):  
Mohamed Said Boulkrane ◽  
Victoria Ilina ◽  
Roman Melchakov ◽  
Mikhail Arisov ◽  
Julia Fedotova ◽  
...  
Keyword(s):  
Mental Health ◽  
Respiratory Illness ◽  
World Health ◽  
Current Review ◽  
Wide Range ◽  
Serious Disease ◽  
Health Organization ◽  
The Impact ◽  
The Brain ◽  
Severe Acute Respiratory Illness

: The World Health Organization declared the pandemic situation caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) in March 2020, but the detailed pathophysiological mechanisms of Coronavirus disease 2019 (COVID-19) are not yet completely understood. Therefore, to date, few therapeutic options are available for patients with mild-moderate or serious disease. In addition to systemic and respiratory symptoms, several reports have documented various neurological symptoms and impairments of mental health. The current review aims to provide the available evidence about the effects of SARS-CoV-2 infection on mental health. The present data suggest that SARS-CoV-2 produces a wide range of impairments and disorders of the brain. However, a limited number of studies investigated the neuroinvasive potential of SARS-CoV-2. Although the main features and outcomes of COVID-19 are linked to severe acute respiratory illness. The possible damages on the brain should be considered, too.

scholarly journals Analysing Tuberculosis Trends in South Asia

2018 ◽  
Vol 8 (6) ◽  
pp. 5245
Author(s):  
Kumar Abhishek ◽  
M. P Singh ◽  
Md. Sadik Hussain
Keyword(s):  
Sri Lanka ◽  
World Health Organization ◽  
South Asia ◽  
Causes Of Death ◽  
Temporal Variations ◽  
World Health ◽  
Spatial And Temporal Variations ◽  
The World ◽  
Health Organization ◽  
The Relationship

<p>Tuberculosis (TB) has been one of the top ten causes of death in the world. As per the World Health Organization (WHO) around 1.8 million people have died due to tuberculosis in 2015. This paper aims to investigate the spatial and temporal variations in TB incident in South Asia (India, Bangladesh, Pakistan, Maldives, Nepal, and Sri-Lanka). Asia had been counted for the largest number of new TB cases in 2015. The paper underlines and relates the relationship between various features like gender, age, location, occurrence, and mortality due to TB in these countries for the period 1993-2012.</p>

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 based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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.

Machine Learning-Based Approach for Predictive Analytics in Healthcare

2022 ◽  
pp. 182-206
Author(s):  
Sandeep Kumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Mortality Rate ◽  
Chronic Diseases ◽  
Domain Knowledge ◽  
Predictive Analytics ◽  
Future Trend ◽  
Medical Data ◽  
World Health ◽  
Premature Deaths ◽  
Health Organization

In this internet era, due to digitization in every application, a huge amount of data is produced digitally from the healthcare sectors. As per the World Health Organization (WHO), the mortality rate due to the various chronic diseases is increasing each day. Every year these diseases are taking lives of at least 50 million people globally, which includes even premature deaths. These days, machine learning (ML)-based predictive analytics are turning out as effective tools in the healthcare sectors. These techniques can extract meaningful insights from the medical data to analyze the future trend. By predicting the risk of diseases at the preliminary stage, the mortality rate can be reduced, and at the same time, the expensive healthcare cost can be eliminated. The chapter aims to briefly provide the domain knowledge on chronic diseases, the biological correlation between theses disease, and more importantly, to explain the application of ML algorithm-based predictive analytics in the healthcare sectors for the early prediction of chronic diseases.

scholarly journals Isoniazid-Induced Acute Psychosis in a Patient with Pleural Tuberculosis

2019 ◽  
Vol 2019 ◽  
pp. 1-3
Author(s):  
Joana Gomes ◽  
Diana Durães ◽  
André Sousa ◽  
Hugo Afonso
Keyword(s):  
Causes Of Death ◽  
Infectious Agent ◽  
World Health ◽  
Psychotic Episode ◽  
Psychiatric History ◽  
Acute Psychosis ◽  
Antituberculous Therapy ◽  
The World ◽  
Health Organization ◽  
Neuropsychiatric Adverse Effects

Tuberculosis is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). Isoniazid is highly bactericidal against replicating tubercle bacilli and is a component of all antituberculous chemotherapeutic regimens currently recommended by the World Health Organization (WHO). Several neuropsychiatric adverse effects, following both therapeutic and overdose use of isoniazid, have been described and isoniazid-induced psychosis, although uncommon, has been reported in the literature. We describe the case of a 21-year-old black woman, with no prior psychiatric history, who developed a psychotic episode four days after she was started on isoniazid. This case highlights psychosis arising as a possible adverse effect of isoniazid and the importance of remaining vigilant when antituberculous therapy is started.

scholarly journals Vulvar Tuberculosis—A Rare Manifestation of the Disease

2019 ◽  
Vol 41 (09) ◽  
pp. 575-578 ◽  
Author(s):  
Cristina Bragança ◽  
Inês Gonçalves ◽  
Luísa Guerreiro ◽  
Maria Janeiro
Keyword(s):  
Rare Case ◽  
Causes Of Death ◽  
World Health ◽  
Histological Findings ◽  
Chain Reaction ◽  
Rare Manifestation ◽  
The World ◽  
History Of ◽  
Polymerase Chain ◽  
Health Organization

AbstractTuberculosis is an infectious disease caused by Mycobacterium tuberculosis. According to data from the World Health Organization, this disease remains one of the leading causes of death worldwide. Although it most commonly affects the lungs, tuberculosis can compromise any organ. The present study reports a rare case of vulvar tuberculosis in a postmenopausal woman with a history of asymptomatic pulmonary and pleural tuberculosis, with no prior documented contact with the bacillus. Diagnosis was based on vulvar lesion biopsies, with histological findings suggestive of infection and isolation of M. tuberculosis by microbiological culture and polymerase chain reaction (PCR) essays. The lesions reverted to normal after tuberculostatic therapy.

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