Early diagnosis of dengue disease using fuzzy inference system

Saikia, Darshana; Dutta, Jiten Chandra

doi:10.1109/microcom.2016.7522513

Cited by 27 publications

(15 citation statements)

References 5 publications

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“…Faisal et al [59] employed an Adaptive Neuro-Fuzzy Inference System to predict the degree of risk of dengue patients. Saikia and Dutta [60] applied FIS to diagnosis the Dengue disease. Alrashoud [61] proposed a Hierarchical Fuzzy Inference system for dengue fever diagnosis.…”

Section: Fuzzy Inference Systems: Background and Related Workmentioning

confidence: 99%

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Shatnawi¹,

Shatnawi²,

Alshara³

et al. 2021

IJACSA

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The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases.

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Section: Fuzzy Inference Systems: Background and Related Workmentioning

confidence: 99%

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Shatnawi¹,

Shatnawi²,

Alshara³

et al. 2021

IJACSA

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“…Such global pandemic urgently requires collaboration among researchers and practitioners at both regional and global scales. The awareness regarding an attempt to monitor and control dengue in Taiwan can be noticed through many proposals adopting various state-of-theart machine intelligence techniques including fuzzy inference system [29], multivariate logistic regression analysis [30], and decision tree induction [31]. To forecast dengue outbreak in southern China, researchers apply GAM [32], SVR as well as variations of regression modeling [33], and the factor-detector modeling module of the ArgGIS software [34].…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

Kerdprasop¹,

Kerdprasop²

2020

Adv. sci. technol. eng. syst. j.

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Dengue is a viral infection disease transmitted to people through the bite of specific mosquito species living in a tropical zone. According to the World Health Organization, dengue has been listed among the top-ten diseases for 2019 as it makes 3.9 billion people in 128 countries be at risk of infection. One major cause of substantial dengue widespread is the globally warm climate that accelerates rapid growth of mosquito vectors. In this research, we aim to build data-driven models to predict dengue cases using satellite index data to represent temperature, humidity, and greenness over the surface area of Bangkok, which is our target area of dengue prediction because of its high infection cases. Oceanic Niño Index is also used as a predictor variable to represent climate variability. The modeling methods employ seven algorithms from two broad schemes of the machine learning field. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are algorithms from the subfield of computational intelligence, whereas multiple linear regression (MLR), generalized linear model (GLM), support vector regression (SVR), classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) are from the statistical learning subfield. Performances of these algorithms are evaluated on the same set of out-of-sample data. The results are that ANFIS is the best model for predicting dengue outbreak in the capital city of Thailand.

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“…Fuzzy Inference System (FIS) [10] was designed to early diagnosis of dengue disease. The most commonly used artificial intelligence in medicine (AIM) system is that a patient can obtain a definitive conclusion by using certain data.…”

Section: Litreture Reviewmentioning

confidence: 99%

A Novel Method for Dengue Management Based on Vital Signs and Blood Profile

Briyatis,

Premaratne,

Silva

2019

IJEAT

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Dengue a mosquito-borne viral disease has become a significant cause of morbidity and mortality in Sri Lanka over the past few years. Based on the symptoms signs and investigations, dengue can be classified as Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS). Since it is a viral infection, medications have not been developed yet. Attempts to develop a vaccine in the prevention has not been completed yet. Our research is based on Cased-Based Reasoning (CBR) to develop an Online decision support system to manage dengue illness. CBR is one method that is capable of reasoning or solving problems on a case that has existed as a solution to a new problem. The CBR system identified the most critical vital signs, parameters, and investigations in different possible situations in DHF and DSS and designed six distinct cases. Clinical parameters and values or ranges of those parameters are used for this research with the guidance of Physicians. The system is developed based on index cases and rules. The system will predict the current situation of the patient by analyzing his/her past and present vital signs and investigations. Usually, DHF patients are monitored hourly, and important monitoring parameters will be entered into the system. The system will then display the current clinical stage and impending issues. Suggestions in the management are indicated. Using this system medical personnel can see how is the Pulse Pressure, Urine Output, Packed Cell Volume (PCV), Platelet Count and White Cell Count (WCC) vary according to the clinical stage of a particular patient. The evaluation of the system was carried out using some past cases of dengue in different stages. Our result shows that the CBR approach can gain significant accuracy in dengue management.

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Early diagnosis of dengue disease using fuzzy inference system

Cited by 27 publications

References 5 publications

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

A Novel Method for Dengue Management Based on Vital Signs and Blood Profile

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