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Computer-Aided Intelligent System for Diagnosing Pediatric Asthma

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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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Acknowledgement

I would like to appreciate all who gave me the possibility to complete this paper. I want to thank the physicians and staff of Immunology, Asthma & Allergy Research center for their collaboration in providing necessary knowledge. I thank Dr. Teymorian for providing papers and guidelines, and Dr. Fazlollahi for their guidance, Mehdi Taherian and Nahid Taherian for contribution in knowledge representation. I appreciate Fariba Zolnoori for interviewing the patients, Ahamad Zolnoori, Esmat Ebrahimi, and Leila Zolnoori as my sponsors, Raziye Maassoumi, and Atiye Bohrani for their general information, and all staff and physicians of Masih Daneshvari Hospital.

Author information

Authors and Affiliations

  1. Mathematic and informatics group, Academic Center for Education, Culture and Research (ACECR), Tarbiat Modares University, P. O. Box 14115-343, Tehran, Iran

    Maryam Zolnoori

  2. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Hossein Fazel Zarandi

  3. Immunology, Asthma and Allergy Research Institute, Tehran University of Medical Sciences, Tehran, Iran

    Mostafa Moin

  4. Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Hassan Heidarnezhad

  5. Department of Biostatistics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran

    Anoshirvan Kazemnejad

Authors
  1. Maryam Zolnoori
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  2. Mohammad Hossein Fazel Zarandi
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  3. Mostafa Moin
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  4. Hassan Heidarnezhad
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Corresponding author

Correspondence to Maryam Zolnoori.

Appendix

Appendix

Appendix A: Schematic view of semantic network of cause and effect variables of asthma

figure a

Appendix B: Some examples of prognosis rules in the knowledgebase

Examples of predefined rules:

If

Child has dry Coughing without cold

AND

Child has Dyspnoea every times

AND

Coughing is more severe at early of morning (after middle night)

Then

Child has asthma with high degree

If

Child has only Dry Chronic cough for more than 2 weeks without cold

AND

The frequency of cough at night and day is equal

Then

The child has asthma with medium degree

If

Child has allergic rhinitis

AND

Allergy rhinitis is moderate-severe

AND

Type of rhinitis is persistent

AND

Body of mass index > 25 and <30

Then

The possibility of asthma is very_very_high

If

Child has wheeze

AND

FEV1: FVC < 75

Then

Child has asthma with very high degree

Appendix C: Algorithm of inference engine of fuzzy expert system

figure b

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Zolnoori, M., Fazel Zarandi, M.H., Moin, M. et al. Computer-Aided Intelligent System for Diagnosing Pediatric Asthma. J Med Syst 36, 809–822 (2012). https://doi.org/10.1007/s10916-010-9545-5

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  • DOI: https://doi.org/10.1007/s10916-010-9545-5

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