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Numer specjalny 3/2024 vol. 57
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CC BY-SA 3.0
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PRACA POGLĄDOWA
Optimization of diagnostic processes using decision trees in ECG data analysis - lets web system analysis engine
Miroslaw Jarosz 1
,
 
Marcin Kowalski 1
,
 
Bartosz Przysucha 2
 
 
 
Więcej
Ukryj
1
WSEI University
 
2
Lublin University of Technology
 
 
Data nadesłania: 21-06-2024
 
 
Data akceptacji: 16-07-2024
 
 
Data publikacji: 20-08-2024
 
 
JoMS 2024;57(Numer specjalny 3):563-579
DOI: https://doi.org/10.13166/jms/191278
 
 
Artykuł (PDF)
Referencje (16)
 
SŁOWA KLUCZOWE
Internet of Things (IoT)
Categorization Decision Trees
ECG Diagnostics
Medical Data Analysis
Diagnosis Automation
 
DZIEDZINY
_Inne
 
STRESZCZENIE
The article presents the capabilities of the LETS Web system, which uses Internet of Things (IoT) technology to analyze medical data to optimize diagnostic processes. The article focuses on implementing decision tree algorithms that analyze electrocardiogram (ECG) data to identify cardiac conditions. The study used three variants of decision trees that differed in structure and ECG parameters. Each variant was tested for its ability to accurately classify cardiac health conditions ranging from simple arrhythmias to complex arrhythmic changes. The study showed that modifications to the structure of the decision trees significantly affected their effectiveness. The most advanced variant of the tree, using multivariate data analysis, showed the highest efficiency in diagnosing complex conditions. The effectiveness of the different variants of decision trees varied, confirming the importance of selecting the suitable diagnostic model for the specifics of the data and clinical goals.
Licencja
Ta praca jest dostępna na licencji Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA)
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eISSN:2391-789X
ISSN:1734-2031
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