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Evaluation of Pulmonary Function Tests by Using Fuzzy Logic Theory

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Abstract

Pulmonary Function Tests (PFTs) are very important in the medical evaluation of patients suffering from “shortness of breath”, and they are effectively used for the diagnosis of pulmonary diseases, such as COPD (i.e. chronic obstructive pulmonary diseases). Measurement of Forced Vital Capacity (FVC) and Forced Expiratory Flow in the 1st second (FEV1) are very important for controlling the treatment of COPD. During PFTs, some difficulties are encountered which complicate the comparison of produced graphs with the standards. These mainly include the reluctance of the patients to co-operate and the physicians’ weaknesses to make healthy interpretations. Main tools of the diagnostic process are the symptoms, laboratory tests or measurements and the medical history of the patient. However, quite frequently, most of the medical information obtained from the patient is uncertain, exaggerated or ignored, incomplete or inconsistent. Fuzziness encountered during PFT is very important. In this study, the purpose is to use “fuzzy logic” approach to facilitate reliable and fast interpretation of PFT graphical outputs. A comparison is made between this approach and methodologies adopted in previous studies. Mathematical models and their coefficients for the spirometric plots are introduced as fuzzy numbers. Firstly, a set of rules for categorizing coefficients of mathematical models obtained. Then, a fuzzy rule-base for a medical inference engine is constructed and a diagnostic “expert system COPDes” designed. This program, COPDes helps for diagnosing the degree of COPD for the patient under test.

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Acknowledgement

The author thanks wishes to express his gratitude to Dr. Aydin Gencsoy, MD for spirometric measurements, and all the volunteer students of Izzet Baysal University in Turkey for the spirometric tests. The author is also indebted to Prof. Dr. Sinasi OZSOYLU, MD for constructing reference rulebases for the intelligent diagnostic program “COPDes” which is designed for this study.

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Authors and Affiliations

  1. Programming Department, HASTEK Ltd Sti, Konur Sokak. No:41/2, Kizilay, 06640, Ankara, Turkey

    Ümit Üncü

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  1. Ümit Üncü
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Appendix

Appendix

Table 8 Normalized values of fuzzy labels for FVC coefficients of mathematical models derived for 8 test groups
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Table 9 Interview questions for the patient to be answered on the questionnaire for investigating degree of chronic bronchitis, emphysema and asthma
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Fig. 3
figure 3

Membership functions for input and output variables FEV1, FVC and COPDrisk%

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Fig. 4
figure 4

Simulation: Execution of Rule-Base for the subject with FEV1 = 0.7 and FVC = 0.9 by using “Mamdani Defuzzication Method” for inferencing

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Fig. 5
figure 5

Simulation: Full range execution for 0.0 ≤ FEV1 ≤ 1.0 and 0.0 ≤ FVC ≤ 1.0 by using “Mamdani Defuzzication Method”

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Fig. 6
figure 6

a Simulation for a male patient with COPD, b Simulation for a female patient with COPD

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Üncü, Ü. Evaluation of Pulmonary Function Tests by Using Fuzzy Logic Theory. J Med Syst 34, 241–250 (2010). https://doi.org/10.1007/s10916-008-9235-8

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