Establishing the risk of neonatal mortality using a fuzzy predictive model

Nascimento, Luiz Fernando Costa; Rizol, Paloma Maria Silva Rocha; Abiuzi, Luciana B.

doi:10.1590/s0102-311x2009000900018

Cited by 21 publications

(17 citation statements)

References 8 publications

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“…This methodology was used recently in the same city to analyze the average time of hospitalization arising from pneumonia 18 . In the national literature, there are also articles with fuzzy application in establishing the risk of neonatal death 15 , 17 and neonatal resuscitation prediction 20 .…”

Section: Discussionmentioning

confidence: 99%

“…Unlike the classical set theory, in which an element belongs or does not belong to a set, in the theory of fuzzy sets an element may belong to more than one set with different degrees of relevance (varying between zero and one). These fuzzy sets are represented by the relevancy function, whose determination depends on the specialist’s individual perception and the data at hand 17 .…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the fuzzy logic theory has become an important approach in diagnosis systems, prognosis, forecasting models, medical treatment and, more recently, in epidemiology and public health 13 , 15 , 17 , 18 , 20 , 22 , 23 , b . …”

Section: Introductionmentioning

confidence: 99%

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Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

2017

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OBJECTIVE Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.METHODS This is a computational model using fuzzy logic based on Mamdani’s inference method. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxide and apparent temperature, we considered two relevancy functions for each variable with the linguistic approach: good and bad. For the output variable number of hospitalizations for asthma and pneumonia, we considered five relevancy functions: very low, low, medium, high and very high. DATASUS was our source for the number of hospitalizations in the year 2007 and the result provided by the model was correlated with the actual data of hospitalization with lag from zero to two days. The accuracy of the model was estimated by the ROC curve for each pollutant and in those lags.RESULTS In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recorded in São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9). The model output data showed positive and significant correlation (r = 0.38) with the actual data; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 and for particulate matter in lag 1.CONCLUSIONS Fuzzy modeling proved accurate for the pollutant exposure effects and hospitalization for pneumonia and asthma approach.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

2017

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“…A similar fuzzy approach was adopted in studies on diabetic neuropathy (11), neonatal mortality (12), and evaluation of myocardial perfusion (13). Fuzzy logic tools have been applied to classify the severity of pulmonary fibrosis (14) and pneumonia in children (15) and in pediatric emergency diagnoses (16).…”

Section: Discussionmentioning

confidence: 99%

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Nascimento

Rizol

Peneluppi

2014

Braz J Med Biol Res

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Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

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“…Various applications of hierarchical fuzzy inference models can be found in the construction sector (Dikmen et al 2007;Zeng et al 2007;Malinowska 2011) in the chemical industry (Darbra and Casal 2009), in medicine (Nascimento et al 2009), in risk analysis of natural disasters (Tsataltzinos et al 2009;Gallego et al 2004), as well as in management (Nissen 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

The Shipwrecks in Greece are Going Fuzzy: A Study for the Potential of Oil Pollution from Shipwrecks in Greek Waters

Ventikos

Louzis

Koimtzoglou

2013

Human and Ecological Risk Assessment: An International Journal

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This article deals with the development and application of a risk analysis approach regarding the importance and fate of shipwrecks in Greece. In the past few decades the potential harmful effects of wrecks and the materials and substances they carry, remaining on the sea bottom, have been realized and the issue of dealing with them has arisen. The methodology used in this article consists of three steps. The first step is to develop a dedicated database of shipwrecks in Greek waters. The second step focuses on the quasi-quantitative assessment of risk for each shipwreck individually through the introduction of implemented factors that have been modeled into the structure of a hierarchical fuzzy system. The analysis is concluded by determining the sources of uncertainty for the system and implementing an appropriate analysis to determine their relative importance. Finally, geographic information system (GIS) tools are used for the visualization of data and the analysis of results in geographic terms as well as for revealing geographic patterns and relationships.

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Establishing the risk of neonatal mortality using a fuzzy predictive model

Abstract: Abstract

Cited by 21 publications

References 8 publications

Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

The Shipwrecks in Greece are Going Fuzzy: A Study for the Potential of Oil Pollution from Shipwrecks in Greek Waters

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