DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algori...

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Bibliographic Details
Published inIraqi journal for computers and informatics (Online) Vol. 49; no. 2; pp. 73 - 82
Main Authors F. Hashim, Hasanain, JEMEL, Meriam, Ben Azzouna, Nadia
Format Journal Article
LanguageArabic
English
Published University of Information Technology and Communications 30.12.2023
Subjects
dynamic thresholding
ecg
feature selection
genetic algorithm
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Summary:Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified.
ISSN:2313-190X
2520-4912
DOI:10.25195/ijci.v49i2.456