Decision-Making Algorithm for The Diagnosis of Lung Diseases Based on Acoustic Signals

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.

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