Apparatus and method for generating an electrocardiogram verification set

An apparatus for generating an electrocardiogram (ECG) verification set, wherein the apparatus comprises:

• at least a processor;
• and
• a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
• receive digital ECG data;
• convert the digital ECG data into analog ECG data;
• generate an ECG validation set, diagnostic training data, and a test set by dividing the analog ECG data utilizing a validation machine learning model, wherein generating the ECG validation set further comprises:
• receiving validation training data, wherein the validation training data comprises inputs of analog ECG data correlated to outputs of ECG validation sets;
• training, iteratively, the validation machine learning model using the validation training data, wherein training the validation machine learning model includes retraining the validation machine learning model with feedback from previous iterations of the validation machine learning model;
• and
• generating the ECG validation set using the trained validation machine learning model;
• and
• validate a diagnostic machine learning model as a function of the ECG validation set, wherein validating the diagnostic machine learning model comprises:
• iteratively training the diagnostic machine learning model using diagnostic training data;
• inputting the ECG validation set into the diagnostic machine learning model;
• generating performance data associated with the diagnostic machine learning model based on the ECG validation set;
• comparing the performance data to a validation threshold;
• retraining the diagnostic machine learning model as a function of the comparison of the performance data to the validation threshold, wherein retraining the diagnostic machine learning model comprises incorporating additional ECG data in the diagnostic training data;
• and
• accepting the diagnostic machine learning model as a function of the test set;
• and
• generate diagnostic data as a function of accepting the diagnostic machine learning model, wherein the diagnostic data comprises at least an abnormality.