Genetic Algorithm-Based Optimization of Arrhythmia Classification Model

The heart rate typically aligns with the electrical events within the heart, and an electrocardiogram (ECG) captures the electronic signals from the heart as they manifest on the surface of the body. By classifying and predicting the heart rate segments specifically, patients can be resuscitated in time. Currently, most of the commercially available problems on heart rate detection use machine learning and deep learning for automatic classification, and algorithms in related directions are slightly inadequate, resulting in still low accuracy and performance of classification and prediction models in specific situations. In this study, an arrhythmia classification algorithm model is developed for specific six heart rate risk classes by analyzing ECG data. A pruned and optimized decision tree method was first used to determine the danger classes of ECG data to attain real-time assessment and alert for arrhythmias. Then the model was optimized by using genetic algorithm, and by iterating the model parameters and selecting better parameter combinations, the final model accuracy improved by about 6%, the recall rate was increased by 13%, and the F1 value was increased by 16.8%, further improving the comprehensive performance of the model, which will provide reference for subsequent research in related directions and further promote the progress of research in related fields such as heart disease patient monitoring.