Key result
Data mining analysis of ECG records using the CART decision tree algorithm achieved a 76.2% correct classification rate for assessing cardiovascular damage severity in patients with combat trauma.
Why the study?
The study was conducted to develop an objective assessment of the severity of physical status in wounded combatants using ECG records and comprehensive ECG scoring.
Can data mining analysis of ECG records objectively assess the severity of myocardial damage in patients with combat trauma?
Observational
Can data mining analysis of ECG records objectively assess the severity of myocardial damage in patients with combat trauma?
Data mining analysis of ECG records can classify the severity of myocardial damage in combat trauma patients with 76.2% accuracy.
Answers are grounded in this paper and cite their sources.
May aid objective ECG severity assessment in combat trauma; hypothesis-generating and requires prospective validation before clinical use.
Goal of this study is to develop an objective assessment of the severity of the physical status of the wounded combatants based on ECG records using the system of comprehensive ECG scoring. Cluster analysis of module Data Mining (Statictica 10) was carried out to identify homogeneous subgroups in patients with combat trauma based on the composite indicators of functional states of the cardiovascular system. Data-mining analysis was carried out using the CART decision tree algorithm for these composite indicators. The best combination of ECG markers, including their possible discriminator limits for the assessment of the severity of damage and functional state of cardiovascular system was found by the classification and regression trees method. The percentage of correct classification was 76,2.
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Chaikovsky et al. (2019) conducted an observational in Combat trauma. Data mining analysis of ECG records was evaluated on Correct classification of severity of damage and functional state of cardiovascular system. Data mining analysis of ECG records using the CART decision tree algorithm achieved a 76.2% correct classification rate for assessing cardiovascular damage severity in patients with combat trauma.
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