A preliminary dengue fever prediction model based on vital signs and blood profile

Zainee, Norhayati binti Mohd; Chellappan, Kalaivani

doi:10.1109/iecbes.2016.7843530

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…A predictive model using decision trees classifications to detect dengue fever based on clinical history records of particular patient such as fever, fever duration, headache and related laboratory result has been proposed [19] and improved the accuracies of prediction in dengue fever. Another work from [20] use another two variables which are vital sign and blood profile of the dengue patients in dengue management practices for predicting and classify dengue patients. Clinical laboratory variables and patient characteristic can be used to distinguish patients with severe illness from those with mold dengue illness [21].…”

Section: Related Work On Denguementioning

confidence: 99%

Proposed Conceptual Iot-Based Patient Monitoring Sensor for Predicting and Controlling Dengue

Hassan¹,

Salwana²,

Drus³

et al. 2018

IJGDC

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Dengue is an epidemic-disease by mosquito-borne virus that spreads easily in geographically affected areas. Dengue outbreak management system has increasingly being developed in identifying and controlling the spread of dengue but with some limitation. The growing development of wearable Internet of Things (IoT), cloud computing, analytical approaches provide better alternative for dengue prediction and control. Previous literature collecting various parameters for analyzing dengue pattern. However, with the increasing number of analytical solution there is a need to investigate the high related parameters that should be use in analyzing and monitoring dengue outbreak before designing an IoT devices. Besides, there is a need for an alternative to ensure that early warning can be detected by monitoring the patient infected by the dengue, this paper aims to propose a conceptual IoT-based patient monitoring sensor for predicting and controlling dengue outbreak. Therefore, this paper provides a recent review of the latest methods and algorithms used to design wearable sensor for patient monitoring in dengue outbreak. Based on the review, this paper outlines the parameters that will be used in dengue for analyzing purposes. Finally, a conceptual IoT-based patient monitoring sensor were proposed comprising three different sensors to further work with analytical tools for dengue prediction pattern. The proposed conceptual design may help researcher to use the parameter identified for development of IoT sensor for dengue outbreak.

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Section: Related Work On Denguementioning

confidence: 99%

Proposed Conceptual Iot-Based Patient Monitoring Sensor for Predicting and Controlling Dengue

Hassan¹,

Salwana²,

Drus³

et al. 2018

IJGDC

View full text Add to dashboard Cite

show abstract

“…The burden of Vector-borne diseases is highest in tropical and subtropical areas. It accounts for more than 17% of all infectious diseases globally resulting in a rise in the death toll of more than 700000 annually [1,2]. The infectious disease spread by mosquitoes can be caused by any of 3 i.e.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers on Forecasting Dengue Fever Infection

Kaur¹,

Sharma²

2023

Advances in Transdisciplinary Engineering

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Each year Dengue infection causes havoc almost across the entire globe raising the death toll and thus becoming a global burden. With the absence of Global approved vaccine and its scope not limiting itself to tropical regions anymore, it has become a rapidly growing epidemic. Researchers have comprehensively explored mechanisms to predict and diagnose infectious diseases and machine learning has revolutionized the medical field by reducing the computing time in analyzing complex data and finding hidden patterns for accurate predictions. In this study, a comparative machine learning-based analysis is achieved using python based classification to predict dengue fever infection in a person. This paper helps interested researchers choose the most efficient classification technique among the selected machine learning classifiers by not only focusing on accuracy achieved but on overall classification metrics analysis to develop a better Dengue predictive model.

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“…Most of the research work carried out in the literature for the automatic diagnosis of dengue is based on symptoms/vital signs/blood profile data (Binti Mohd Zainee & Chellappan, 2016; Fernández et al, 2016; Hossain et al, 2017; Saikia & Dutta, 2016; Shimpi et al, 2017; Torres et al, 2016; Tuan et al, 2015). Saikia et al designed a fuzzy‐logic‐based system for dengue diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

et al. 2021

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Dengue is a vector-borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour-intensive, tedious, and time-consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue-infected PBSs were acquired at a magnification of 100Â. Grey-level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre-trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand-crafted features can be valuable for dengue diagnosis.

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A preliminary dengue fever prediction model based on vital signs and blood profile

Cited by 8 publications

References 9 publications

Proposed Conceptual Iot-Based Patient Monitoring Sensor for Predicting and Controlling Dengue

Proposed Conceptual Iot-Based Patient Monitoring Sensor for Predicting and Controlling Dengue

Comparative Analysis of Machine Learning Classifiers on Forecasting Dengue Fever Infection

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

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