Cardiomyopathy prediction in patients with permanent ventricular pacing using machine learning methods
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2024.1.03Keywords:
permanent ventricular pacing, risk factors, artificial intelligence, forecasting, machine learningAbstract
Pacing-induced cardiomyopathy is a notable issue in patients needing permanent ventricular pacing. Identifying risk groups early and swiftly preventing the ailment can reduce patient harm. However, current prognostic methods require clarity. We employed machine learning to develop predictive models using medical data. Three algorithms — decision tree, group method of data handling, and logistic regression — formed models that forecast pacing-induced cardiomyopathy. These models displayed high accuracy in predicting development, signifying soundness. Factors like age, paced QRS width, pacing mode, and ventricular index during implantation significantly influenced predictions. Machine learning can enhance pacing-induced cardiomyopathy prediction in ventricular pacing patients, aiding medical practice and preventive strategies.
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