ArticleJournal of medical systems2012
Computer-aided intelligent system for diagnosing pediatric asthma.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The soft computing-based approach to investigate allergic diseases: a systematic review.Clinical and molecular allergy : CMA · 2017Article
- Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2015Article
- Fuzzy rule-based expert system for evaluating level of asthma control.Journal of medical systems · 2012Article
- Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation.Journal of medical systems · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asthma is a lung chronic inflammatory disorder estimated between 1.4% and 27.1% in different area of the world. Result of various studies show that asthma is usually underdiagnosed especially in developing countries, because of limitations on access to medical specialists and laboratory facilities. In this paper, we report on the development and evaluation of a novel patient-based fuzzy system that promotes the diagnosis method of asthma. The design of this application addresses five critical issues included: 1) modular representation of asthma diagnostic variables regard to patients' perception of the disease, 2) algorithmic approaches conducting inference of diagnosing based on patient's response to questions, 4) front-end mechanism for capturing data from patient, 5) output for both patient and physician regard to asthma possibility. for the system output score (0-10) the efficacy of this system calculated in the study sample included 139 asthmatic patients and 139 non-asthmatic patients (age range 6-18) reinforce the sensitivity of 88% and specificity of 100% for cut off value 0.7.
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Registered trials
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