Arrhythmia

as a serious heart disease

often causes harm to people's quality of life and life safety. Therefore

accurately and quickly. This paper focuses on the optimization of AutoML arrhythmia detection model based on genetic algorithm. First

by collecting a large amount of ECG data as training and test samples

and then using automated machine learning (AutoML) technology

the automated construction and optimization of the arrhythmia detection model were realized. Based on this

the genetic algorithm carefully adjusts the parameters of the model and optimizes the performance of the model. The experimental results show that the model provided by this method has high accuracy and high stability in arrhythmia detection

which greatly improves the efficiency and accuracy of arrhythmia identification

while reducing the probability of misdiagnosis. In addition

the model not only simplifies the model building process and improves the work efficiency

but also has a strong generalization performance

which can be applied to the identification and prediction of other similar diseases. Therefore

this study has important clinical value for cardiology diagnosis

especially the early detection and early warning of arrhythmia.