Diagnosis and Prognosis of the Arbovirus-Dengue using Intelligent Algorithm

Jiji, G. Wiselin; Lakshmi, V. S. Ramya; Lakshmi, K. Vathsala; Priya, Swetha

doi:10.1007/s40031-014-0171-3

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Various satellite-based indices such as thermal condition, temperature condition, and greenness of vegetation also appear in the model that complement early detection but it is intended to predict whether or not a high incidence of disease will occur several weeks in the future.The models showed good performance in predicting multi-week dengue incidence 4 weeks in advance. But sudies of India and Vietnam using SMR, SARIMA & Poisson distributed lag model and Zero-inflated Poisson Regression Model have some similar as well as contradictory findings [23,25]. In kolkatta based research it was found that there is an increasing Dengue infection trend observed with the rise in carbon dioxide emission along with a correlation between climatic variables and dengue incidence is observed whereas in Vietnam research findings although this study is in line with previous findings of temperature and relative humidity being significant factors.…”

Section: Dengue Outbreakmentioning

confidence: 99%

Dengue Disease Forecasting: Current Outbreak and Diagnostic Model’s Analysis

Kaur

Sharma

2023

Advances in Transdisciplinary Engineering

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The global burden of infectious Dengue disease remains a tricky bomb as its explosion is no longer restricted to tropical/subtropical regions anymore, also the absence of a globally approved vaccine is still there. Continuous research is done to devise systems that could timely predict, diagnose, and treat patients to effectively lessen the yearly Dengue death toll worldwide which resulted in a regular escalation of research in this area. This paper aims to perform a quick effective review of studies done till December 2020, emphasizing forecasting of Dengue epidemiology covering a range of factors such as diagnostics, surveillance, and prognosis prediction so that the intended reader can get an overall scheme of the current research progress well as obstacles regarding Dengue infection. This study points out numerous predictive approaches to Dengue investigation and several open issues which require further investigation eg: human mobility, mosquito breeding sensory reporting, diagnosis of dengue warning stages which may lead to a more accurate and detailed dengue patient prediction and care model. This paper has a good blend of papers encompassing various research directions both traditional and innovative in Dengue epidemiology which are further sub grouped according to their proposed approach and objectives to enhance understanding.

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Section: Dengue Outbreakmentioning

confidence: 99%

Dengue Disease Forecasting: Current Outbreak and Diagnostic Model’s Analysis

Kaur

Sharma

2023

Advances in Transdisciplinary Engineering

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“…IoT-based sensors and healthcare systems have made the dengue-combating system more resilient, which not only detects infected patients but also, on admittance to a hospital, helps in disease management [38,39]. If included in IoT systems [40,41], artificial intelligence can easily handle the data generated by the sensors, which need the supervision of medical personnel for understanding. Moreover, these techniques effectively detect any anomaly, enhancing the security aspect of IoT-based healthcare systems and making them intelligent [42,43].…”

Section: Cloud Computing and Iot-based Dengue Healthcarementioning

confidence: 99%

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

et al. 2022

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Dengue fever has earned the title of a rapidly growing global epidemic since the disease-causing mosquito has adapted to colder countries, breaking the notion of dengue being a tropical/subtropical disease only. This infectious time bomb demands timely and proper treatment as it affects vital body functions, often resulting in multiple organ failures once thrombocytopenia and internal bleeding manifest in the patients, adding to morbidity and mortality. In this paper, a tool is used for data collection and analysis for predicting dengue infection presence and estimating risk levels to identify which group of dengue infections the patient suffers from, using a machine-learning-based tertiary classification technique. Based on symptomatic and clinical investigations, the system performs real-time diagnosis. It uses warning indicators to alert the patient of possible internal hemorrhage, warning them to seek medical assistance in case of this disease-related emergency. The proposed model predicts infection levels in a patient based on the classification provided by the World Health Organization, i.e., dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, acquiring considerably high accuracy of over 90% along with high sensitivity and specificity values. The experimental evaluation of the proposed model acknowledges performance efficiency and utilization through statistical approaches.

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“…The new Arbovirus Dengue Diagnosis Fusion Architecture was introduced in [7]. The software incorporates characteristics of platelets and CBR to promote the evaluation of the health system.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Convolutional Neural Network with PSO Based Severe Dengue Prognosis Method in Human Genome Data

Mustafa¹,

Ahmed²,

Eljack³

2020

J Comput Sci Intell Technol

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Dengue is one of the most significant diseases transmitted by arthropods in the world. Dengue phenotypes are focused on documented inaccuracies in the laboratory and clinical studies. In countries with a high incidence of this disease, early diagnosis of dengue is still a concern for public health. Deep learning has been developed as a highly versatile and accurate methodology for classification and regression, which requires small adjustment, interpretable results, and the prediction of risk for complex diseases. This work is motivated by the inclusion of the Particle Swarm Optimization (PSO) algorithm for the fine-tuning of the model's parameters in the convolutional neural network (CNN). The use of this PSO was used to forecast patients with extreme dengue, and to refine the input weight vector and CNN parameters to achieve anticipated precision, and to prevent premature convergence towards local optimum conditions.

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Diagnosis and Prognosis of the Arbovirus-Dengue using Intelligent Algorithm

Cited by 4 publications

References 21 publications

Dengue Disease Forecasting: Current Outbreak and Diagnostic Model’s Analysis

Dengue Disease Forecasting: Current Outbreak and Diagnostic Model’s Analysis

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

Hybrid Convolutional Neural Network with PSO Based Severe Dengue Prognosis Method in Human Genome Data

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