Redes neurais artificiais aplicadas no estudo de questionário de varredura para conjuntivite alérgica em escolares

Goulart, Denise Atique; Tacla, Milena Atique; Marback, Patrícia Maria Fernandes; Solé, Dírceu; Paranhos, Augusto; Perez, Homero B.; Freitas, Denise de; Sato, Élcio Hideo

doi:10.1590/s0004-27492006000500017

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The trained neural network achieved an accuracy of 88, 31%. In another work Goulart et al [32] proposed the ANN to study an allergic conjunctivitis screening questionnaire. In this work the ANN predicted allergic diagnosis in 100% of cases using 7 of the 15 existent items.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies provided results about SC systems focused on early diagnosis of allergic diseases and the classification of illness categories (i.e., exacerbation, severity) obtaining a mean accuracy of 86.5%. More specifically, this review revealed that the SC approach could have an important impact on the analysis of an enormous amount of screening questionnaires and in the prediction of allergic diseases, discovering patterns deeply hidden within the still-unexplored data [32, 33]. This is possible because the SC models are flexible and able to generalize and predict on an individual basis the probability of diagnosis related to the specific disease of questionnaire respondents.…”

Section: Discussionmentioning

confidence: 99%

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The soft computing-based approach to investigate allergic diseases: a systematic review

et al. 2017

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BackgroundEarly recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.ObjectiveThe aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.MethodsThe review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.ResultsThe review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause–effect relationships, which make it difficult to assess the evolution of disease.ConclusionsAlthough most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The soft computing-based approach to investigate allergic diseases: a systematic review

et al. 2017

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“…A capacidade das RNA de aprender com exemplos reais e de reconhecer situações semelhantes àquelas utilizadas no seu aprendizado/ treinamento despertou o interesse de pesquisadores em várias áreas do conhecimento, como no processamento e na interpretação de imagens [7][8][9][10] , automação e controle [11][12][13] , séries temporais 14,15 , tratamento de efluentes 16 , auxílio a diagnóstico médico [17][18][19][20] , nutrição e alimentos [21][22][23][24][25][26] , entre muitos outros.…”

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Utilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitário

2011

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OBJETIVO: Construir uma rede neural artificial para auxiliar os gestores de restaurantes universitários na previsão de refeições diárias. MÉTODOS: O estudo foi desenvolvido a partir do levantamento de oito variáveis que influenciam o número de refeições diárias servidas no restaurante universitário. Utiliza-se o algoritmo de treinamento Backpropagation. Os resultados por meio da rede são comparados com os da série estudada e com resultados da estimação por média aritmética simples. RESULTADOS: A rede proposta acompanha as inúmeras alterações que ocorrem no número de refeições diárias do restaurante universitário. Em 73% dos dias analisados, o método das redes neurais artificiais apresenta uma taxa de acerto maior do que o método da média aritmética simples. CONCLUSÃO: A rede neural artificial mostrou-se mais adequada para a previsão do número de refeições do que a metodologia de média simples ou quando a decisão do número de refeições é feita de forma subjetiva, sem critérios científicos.

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“…Among the areas of application of medical informatics, artificial intelligence (AI) uses artificial neural networks (ANN) to search for a computational model based on biological neurons which, by means of mathematical models, generates an artificial neuron, and through interconnections of various artificial neurons, generates an ANN. Its importance lies in solving problems that are not easily solved by conventional techniques and tools, such as pattern recognition 13 , 14 , 15 ; problem solving in classification, prediction, approximation, categorization and optimization; character and voice recognition; and predictions of time series 11 , 13 , 16 , 17 , 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis Based on Artificial Neural Networks for Aiding in Diagnosing Osteoarthritis of the Lumbar Spine

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Santos

et al. 2011

Revista Brasileira de Ortopedia (English Edition)

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Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.

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Redes neurais artificiais aplicadas no estudo de questionário de varredura para conjuntivite alérgica em escolares

Cited by 8 publications

References 13 publications

The soft computing-based approach to investigate allergic diseases: a systematic review

The soft computing-based approach to investigate allergic diseases: a systematic review

Utilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitário

Computational Analysis Based on Artificial Neural Networks for Aiding in Diagnosing Osteoarthritis of the Lumbar Spine

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