Detection and analysis of disease entities based on lung conditions

Abstract

The article presents a method for detecting and analysing disease entities associated with lung diseases. The results are related to work on the design of a medical diagnostic system based on impedance tomography. One of the key features of the solution is its ability to diagnose respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS) and pneumothorax (PTX). The article describes the results of a classification model that effectively distinguishes between healthy and sick patients, achieving an impressive accuracy of 99.86%. This result underscores the robustness and reliability of the model. The conclusions of the presented research can serve as a basis for further work on improving diagnostic methods and introducing innovative healthcare solutions for patients with respiratory diseases, which may enable faster and more accurate diagnoses of lung diseases and provide more effective treatment and care for patients.

Keywords

• electrical impedance tomography
• chronic obstructive pulmonary disease
• acute respiratory distress syndrome
• pneumothorax
• classification and regression trees