N 3 (181) 2022. P. 53–58

APPLICATION OF NEURON NETWORKS FOR DIAGNOSTIC OF PULMONARY DISEASES

O. М. Komlevoi

Odesa National Medical University, Odesa, Ukraine

DOI 10.32782/2226-2008-2022-3-12

Introduction. Neural networks are used to study and diagnose pulmonary diseases based on different sets of input data. In this work, a neural network was built for diagnosing COPD by classifying the state of the respiratory system.

Material and methods. An extended system of diagnostic indicators is used for diagnosis, which, in addition to the results of a general clinical analysis, a questionnaire based on defined standards, and biochemical indicators, also includes biophysical indicators. Biophysical indicators are percentage contributions in the moisture condensate composition of exhaled air particles of different origins and sizes. The group of patients consisted of 186 people aged from 40 to 45 years, patients with COPD in the exacerbation stage during inpatient treatment, during (on the 5th day) and after treatment (on the 11-14th day) at the hospital stage, according to the existing protocol. During the examination, in addition to standard methods of examination, samples of moisture condensate of exhaled air were obtained from patients using a special device. The comparison group consisted of 373 practically healthy people aged 30 to 37 years. The values of all diagnostic indicators are checked for quality before their use, cases of data lag in time, desynchronization, appearance of non-normalized data, omissions, irrelevance, appearance of raw data and their duplication are monitored. When low-quality data is detected, appropriate technical and organizational practices are applied to them, which aim to correct the situation. The obtained qualitative input data are divided into 3 groups: for training, validation and testing of the neural network in a ratio of 3:1:1.

Conclusion. The neural network, which was built to diagnose COPD, showed high quality of the obtained solutions. Performance analysis of the built model included accuracy, completeness, and F-measure values, which were found to be the best at 30,000 epochs of neural network training. An attempt to use the same neural network to study the state of the respiratory system after the completion of treatment of patients with COPD confirmed that in patients who are at this stage, the state of the bronchopulmonary system does not have time to fully recover. This happens due to the complication of restoration of the homeostatic mechanisms of the functioning of respiratory tract tissues. Therefore, the bronchopulmonary system of patients immediately after treatment cannot be considered completely healthy.

Key words: pulmonology, COPD, diagnostics, biophysical indicators.

REFERENCES

  1. Boutaba R,Salahuddin MA, Limam N, et al. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities, Journal of Internet Services and Applications, 2018, Volume 9, Article Number 16. DOI: 10.1186/s13174-018-0087-2.
  2. Koteluk O, Wartecki A, Mazurek S, Kolodziejczak I, Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine, Journal of Personalized Medicine, 2021, Volume 11, Issue 1, Article Number 32. DOI: 10.3390/jpm11010032.
  3. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey, Heliyon, Volume 4, Issue 11, Article Number e00938. DOI: 10.1016/j.heliyon.2018.e00938
  4. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to Machine Learning, Neural Networks, and Deep Learning, Translation Vision Science & Technology, 2020, Volume 9, Issue 2, Article Number 14. DOI: 10.1167/tvst.9.2.14.
  5. Spathis D, Vlamos Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics Journal, 2019, Volume 25, Issue 3, Pp. 811-827. DOI: 10.1177/1460458217723169.
  6. Suresh KM, Perumal V, Yuvaraj G, Rajasekar SJS. Detection of Pneumonia from Chest X-Ray images using Machine Learning, Concurrent Engineering-Research and Applications, 2022, Article Number 1063293X221106501. DOI: 10.1177/1063293X221106501.
  7. Wang DW, Willis DR, Yih Y. The pneumonia severity index: Assessment and comparison to popular machine learning classifiers, International Journal of Medical Informatics, 2022, Volume 163, Article Number 104778. DOI: 10.1016/j.ijmedinf.2022.104778.
  8. Yu G, Li ZM, Li SX,et al. The role of artificial intelligence in identifying asthma in pediatric inpatient setting, Annals of Translational Medicine, 2020, Volume 8, Issue 21, Article Number 1367. DOI: 10.21037/atm-20-2501a.
  9. Chopde NR, Miri R. A Novel Machine Learning Approach for Prediction of Chronic Obstructive Pulmonary Disease, Bioscience Biotechnology Research Communications, 2020, Volume 13, Issue 15, Pp. 285-291. DOI: 10.21786/bbrc/13.15/50.
  10. Zeng SY, Arjomandi M, Luo G. Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study, JMIR Medical Informatics, 2022, Volume 10, Issue 2, Article Number e33043. DOI: 10.2196/33043.
  11. Bloom CI, Ricciardi F, Smeeth L, Stone P, Quint JK. Predicting ХОЗЛ 1-year mortality using prognostic predictors routinely measured in primary care, BMC Medicine, Volume 17, Article Number 73. DOI: 10.1186/s12916-019-1310-0.
  12. Becirovic LS, Deumic A, Pokvic LG, Badnjevic A. Aritificial Inteligence Challenges in COPD management: a review, 2021 IEEE 21st International Conference On Bioinformatics And Bioengineering (IEEE BIBE 2021). DOI: 10.1109/BIBE52308.2021.9635374.
  13. Komleva NO, Cherneha KS, Tymchenko BI, Komlevoy Intellectual Approach Application for Pulmonary Diagnosis, IEEE First International Conference «Data Stream Mining & Processing», Lviv Ukraine, 2016, pp. 48–52.
  14. Komlevaya N, Komlevoy A, Chernega Designing of the specialized computer system for making pulmonology diagnosis,in: Proceedings of the 8th International Conference of Programming UkrPROG’2014, Kyiv Ukraine, 2014, pp. 253–263.
  15. Komlevoi O.,  Komleva N.,  Liubchenko V.,  Zinovatna S.Biological Data Mining and Its Applications in Pulmonology. Proceedings of the 4th International Conference on Informatics & Data-Driven Medicine. Valencia, Spain, November 19 – 21, 2021. Vol-3038. p. 44-53. http://ceur-ws.org/Vol-3038/paperpdf
  16. Krisilov VA, Komleva NO. Analysis and Evaluation of Competence of Information Sources in Problems of Intellectual Data Processing. Problemele Energeticii Regionale.  2019. Issue: 1-1. Special Issue: SI. Pp. 91-104. https://doi.org/10.5281/zenodo.3239185
  17. Komleva N , Liubchenko V, Zinovatna S. Evaluation of the Quality of Survey Data and its Visualization Using Dashboards. 15th International Scientific and Technical Conference «Computer Science and Information Technologies» Lviv Polytechnic National University. Lviv, Ukraine, September 23-26, 2020. Vol. 2. – Lviv, 2020. – P. 234–237. DOI: 10.1109/CSIT49958.2020.9321970.
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