Published 2026-03-31
Keywords
- Diagnosis of lung diseases,
- auscultation,
- decision-making algorithm
How to Cite
Copyright (c) 2026 T. M. Magrupov, N. S. Yusupova

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This paper presents a formalized algorithm for diagnosing lung diseases based on the comprehensive interpretation of auscultatory data. The algorithm covers all stages of the diagnostic process, from the collection of clinical information to confirmation of the diagnosis and formulation of recommendations. Particular attention is given to the integration of heterogeneous data and their classification into the main nosological groups. The proposed approach can be used both in clinical practice and as part of automated intelligent clinical decision support systems. The results of the study provide a methodological foundation for the development of multimodal diagnostic systems using machine learning methods.
References
- Мagrupov Talat, Akhmedzhanov Ravshanjon, Magrupova Maloxat, Radjabov Akhmedkhan, Abdihalikov Seit, Gaibnazarov Sardorkhon. Neural Network Models for Diagnosing Lung Diseases. Proceedings of the 2025 International Conference on Systems and Technologies of the Digital HealthCare (STDH – 2025) June 2-6, 2025. Saint Petersburg, Russia 2025. p. 287 -290 https://ieeexplore.ieee.org/document/11227492
- Т.М. Magrupov, Н.М. Nurillayeva, R.R. Akhmadjonov, S.S. Zubaydullaev, S.S. Gaibnazarov, Е.А. Semenova. Algorithmic and software implementation of biomedical imaging classification technology for lung diseases. Biomedical Engineering. Vol.59. 2025 № 2. Р. 99-103. DOI https://doi.org/10.1007/s10527-025-10471-x https://www.scopus.com/pages/publications/105012454614?origin=resultslist
- Magrupov T.M., Nurillaeva N.M., Zubaidullaeva M. T., Yarmukhamedova D.Z., Talatov E.T., Semenova E.A. “Study of relationship between measures of heart rate variability and the frequencies of various types of arrhythmias in patients with arterial hypertension” Biomedical Engineering, 2025, 58(6), p 391–393. DOI: 10.1007/s10527-025-10441-3 https://www.scopus.com/pages/publications/105003026781?origin=resultslist
- World Health Organization. Global surveillance of chronic respiratory diseases. Geneva: WHO Press; 2023.
- Magrupov T. M., Nazirov R. M., Abdullaev I. N. Formation of a database of lung disease sound signals. Science and Innovation. International scientific journal Volume 3 Issue 9 September 2024. p. 90-96. ISSN: 2181-3337 | Scientists.UZ https://doi.org/10.5281/zenodo.13880716
- Pahar M, Klopper M, Warren R, Niesler T. COVID-19 cough classification using machine learning and global smartphone recordings. IEEE J Biomed Health Inform. 2020;25(3):1–10.
- Ribeiro MT, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. Proc ACM SIGKDD. 2016:1135–1144