scholarly journals Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Annamalai R ◽  
Nedunchelian R
Keyword(s):  
Diabetes Mellitus ◽  
State Of The Art ◽  
Disease Prediction ◽  
Severity Level ◽  
Training Time ◽  
The World ◽  
Positive Dataset ◽  
Diabetes Prediction ◽  
Two Phases ◽  
F Measure

Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

scholarly journals Underlying Chronic Disease and COVID-19 Infection: A State-of-the-Art Review

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Habib Haybar ◽  
Khalil Kazemnia ◽  
Fakher Rahim
Keyword(s):  
Public Health ◽  
Diabetes Mellitus ◽  
Cardiovascular Disease ◽  
Chronic Disease ◽  
State Of The Art ◽  
Control Measures ◽  
Healthcare Staff ◽  
Public Health Services ◽  
Infection Control Measures ◽  
The World

Context: In late December 2019, a new coronavirus, called COVID-19 (SARS-CoV-2/2019-nCoV), triggered the outbreak of pneumonia from Wuhan (Han’s seafood market) in China, which is now possessing major public health threats to the world. The objective of this review was to describe the epidemiology of COVID-19 in different chronic diseases and understand the pathophysiological mechanisms by which the virus can lead to the progression of these diseases. Results: The prevalence of COVID-19 infection has become a clinical threat to the general population and healthcare staff around the world. However, knowledge is limited about this new virus. The most commonly reported conditions are diabetes mellitus, chronic lung disease, and cardiovascular disease. Conclusions: Effective antiviral therapy and vaccination are currently being evaluated and under-development. What we can do now is the aggressive implementation of infection control measures to prevent the human-human transmission of SARS-CoV-2. Public health services should also monitor the situation. The more the knowledge about this new virus and its prevalence, the better the ability of us to deal with it. It is hoped that we will overcome COVID-19 soon with the discovery of effective vaccines, drugs, and treatments.

Association and treatment of diabetes in patients affected by Covid-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1198-1201
Author(s):  
Syed Yasir Afaque
Keyword(s):  
Diabetes Mellitus ◽  
Prognostic Value ◽  
Heart Diseases ◽  
The Novel ◽  
The World ◽  
Treatment Of Diabetes ◽  
Coronavirus Infection ◽  
Composite Outcomes ◽  
Novel Coronavirus ◽  
The Impact

In December 2019, a unique coronavirus infection, SARS-CoV-2, was first identified in the province of Wuhan in China. Since then, it spread rapidly all over the world and has been responsible for a large number of morbidity and mortality among humans. According to a latest study, Diabetes mellitus, heart diseases, Hypertension etc. are being considered important risk factors for the development of this infection and is also associated with unfavorable outcomes in these patients. There is little evidence concerning the trail back of these patients possibly because of a small number of participants and people who experienced primary composite outcomes (such as admission in the ICU, usage of machine-driven ventilation or even fatality of these patients). Until now, there are no academic findings that have proven independent prognostic value of diabetes on death in the novel Coronavirus patients. However, there are several conjectures linking Diabetes with the impact as well as progression of COVID-19 in these patients. The aim of this review is to acknowledge about the association amongst Diabetes and the novel Coronavirus and the result of the infection in such patients.

Mechanisms Involved in Glycemic Control Promoted by Exercise in Diabetics

2019 ◽  
Vol 15 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Eric Francelino Andrade ◽  
Víviam de Oliveira Silva ◽  
Débora Ribeiro Orlando ◽  
Luciano José Pereira
Keyword(s):  
Diabetes Mellitus ◽  
Metabolic Disease ◽  
Skeletal Muscle ◽  
Glycemic Control ◽  
Sedentary Lifestyle ◽  
World Population ◽  
Pharmacological Interventions ◽  
High Prevalence ◽  
The World ◽  
Main Factors

Introduction: Diabetes mellitus is a metabolic disease characterized by high glycemic levels for long periods. This disease has a high prevalence in the world population, being currently observed an increase in its incidence. This fact is mainly due to the sedentary lifestyle and hypercaloric diets. Non-pharmacological interventions for glycemic control include exercise, which promotes changes in skeletal muscle and adipocytes. Thus, increased glucose uptake by skeletal muscle and decreased insulin resistance through modulating adipocytes are the main factors that improve glycemic control against diabetes. Conclusion: It was sought to elucidate mechanisms involved in the improvement of glycemic control in diabetics in front of the exercise.

Yoga in the management of Diabetes Mellitus

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Sreeharsha N. ◽  
Bargale Sushant Sukumar ◽  
Divyasree C. H.
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Eating Habits ◽  
Cost Effective ◽  
The Body ◽  
Positive Thinking ◽  
The World ◽  
Diabetic Population ◽  
Major Factors ◽  
Excess Glucose

Diabetes mellitus is a chronic metabolic disorder in which the body is unable to make proper utilisation of glucose, resulting in the condition of hyperglycaemia. Excess glucose in the blood ultimately results in high levels of glucose being present in the urine (glycosuria). This increase the urine output, which leads to dehydration and increase thirst. India has the largest diabetic population in the world. Changes in eating habits, increasing weight and decreased physical activity are major factors leading to increased incidence of Diabetes. Lifestyle plays an important role in the development of Diabetes. Yoga offers natural and effective remedies without toxic side-effects, and with benefits that extend far beyond the physical. This system of Yoga is a simple, natural programme involving five main principles: proper exercise, proper breathing, proper relaxation, proper diet and positive thinking and meditation. It is a cost effective lifestyle intervention technique.

scholarly journals D-Penicillamine: The State of the Art in Humans and in Dogs from a Pharmacological and Regulatory Perspective

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Michela Pugliese ◽  
Vito Biondi ◽  
Enrico Gugliandolo ◽  
Patrizia Licata ◽  
Alessio Filippo Peritore ◽  
...  
Keyword(s):  
Veterinary Medicine ◽  
State Of The Art ◽  
European Medicines Agency ◽  
Therapeutic Goals ◽  
First Choice ◽  
Regulatory Perspective ◽  
The World ◽  
United States Food ◽  
States Food ◽  
Food And Drugs Administration

Chelant agents are the mainstay of treatment in copper-associated hepatitis in humans, where D-penicillamine is the chelant agent of first choice. In veterinary medicine, the use of D-penicillamine has increased with the recent recognition of copper-associated hepatopathies that occur in several breeds of dogs. Although the different regulatory authorities in the world (United States Food and Drugs Administration—U.S. FDA, European Medicines Agency—EMEA, etc.) do not approve D-penicillamine for use in dogs, it has been used to treat copper-associated hepatitis in dogs since the 1970s, and is prescribed legally by veterinarians as an extra-label drug to treat this disease and alleviate suffering. The present study aims to: (a) address the pharmacological features; (b) outline the clinical scenario underlying the increased interest in D-penicillamine by overviewing the evolution of its main therapeutic goals in humans and dogs; and finally, (c) provide a discussion on its use and prescription in veterinary medicine from a regulatory perspective.

scholarly journals Uncovering two phases of early intercontinental COVID-19 transmission dynamics

2020 ◽  
Vol 27 (8) ◽  
Author(s):  
Jing Yang ◽  
Juan Li ◽  
Shengjie Lai ◽  
Corrine W Ruktanonchai ◽  
Weijia Xing ◽  
...  
Keyword(s):  
Phylogenetic Analyses ◽  
International Travel ◽  
Contact Tracing ◽  
Genetic Sequencing ◽  
Response Strategies ◽  
The World ◽  
Containment Measures ◽  
The Usa ◽  
Community Transmission ◽  
Two Phases

Abstract Background The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. Methods We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. Results Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. Conclusions Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

2021 ◽  
Vol 24 ◽  
pp. 157-166
Author(s):  
Wilailuck Tuntayothin ◽  
Stephen John Kerr ◽  
Chanchana Boonyakrai ◽  
Suwasin Udomkarnjananun ◽  
Sumitra Chukaew ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Prediction Model ◽  
Disease Prediction ◽  
Development And Validation

scholarly journals Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

2021 ◽  
Vol 2 (2) ◽  
pp. 1-18
Author(s):  
Hongchao Gao ◽  
Yujia Li ◽  
Jiao Dai ◽  
Xi Wang ◽  
Jizhong Han ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Representation ◽  
Text Recognition ◽  
Natural Scene ◽  
Attention Network ◽  
Training Time ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Local Representations ◽  
Scene Text Recognition

Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.

scholarly journals Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

2019 ◽  
Vol 9 (12) ◽  
pp. 2535
Author(s):  
Di Fan ◽  
Hyunwoo Kim ◽  
Jummo Kim ◽  
Yunhui Liu ◽  
Qiang Huang
Keyword(s):  
Human Computer Interaction ◽  
Video Surveillance ◽  
State Of The Art ◽  
Learning Task ◽  
Face Verification ◽  
Task Learning ◽  
The World ◽  
In The Wild ◽  
And Gender ◽  
Learning Architectures

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

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