A Machine Learning Protocol to Screen For Atrial Fibrillation
Authors: Amina Khalpey, PhD, Brynne Rozell, BS, Zain Khalpey, MD, PhD, FACS
Atrial fibrillation (AF) is a common heart arrhythmia that increases the risk of heart failure. AF can result in the loss of atrial contractility, which is responsible for about 15-30% of the total cardiac output. The loss of atrial contraction can lead to decreased cardiac output and decreased perfusion to organs.. AF can also lead to irregular and rapid ventricular rates, which can cause the heart muscle to work harder and may lead to myocardial damage over time. This increased workload can also result in the heart muscle becoming thickened and stiff, which can decrease the heart’s ability to pump effectively and result in heart failure. Additionally, atrial fibrillation can lead to the development of tachycardia-induced cardiomyopathy (TIC), a reversible form of heart failure. TIC can occur when the heart beats too fast for an extended period, causing it to become overworked and dilated. This can lead to weakened heart muscle and impaired pumping function, which can result in heart failure.
Atrial Fibrillation Is a Major Cause of Heart Failure
Early detection and intervention are critical to prevent heart failure in patients with atrial fibrillation. The use of machine learning algorithms can be an effective tool for predicting heart failure in these patients. By predicting heart failure, patients can be treated earlier which will greatly improve outcomes and save millions in health services each year. Many researchers have been searching for how to predict and prevent heart failure, and now machine learning may be the perfect tool to make this goal a reality. Our research team has developed a protocol to screen for atrial fibrillation using artificial intelligence and machine learning.
Khalpey Lab Afib Screening Protocol: Materials and Methods:
Data Collection: A large and diverse dataset of ECG recordings from patients with atrial fibrillation and heart failure should be collected. The recordings should be labeled as either heart failure or normal.
Data Preprocessing: The data should be preprocessed to remove any artifacts and normalize the signals.
Data Splitting: The preprocessed data should be split into training, validation, and testing sets.
Model Selection: A suitable machine learning algorithm, such as a decision tree, random forest, or support vector machine, should be chosen.
Model Training: The selected machine learning algorithm should be trained using the training data. The hyperparameters should be adjusted to optimize performance.
Model Validation: The model’s performance should be evaluated on the validation set and adjusted as necessary.
Model Testing: The model should be tested on the testing set to determine its overall accuracy.
Model Deployment: If the model performs well, it can be deployed in a clinical setting to predict heart failure in patients with atrial fibrillation.
Machine Learning Can Diagnose and Prevent Heart Failure
This protocol provides a systematic approach for screening atrial fibrillation with a machine learning tool to predict heart failure. The use of machine learning algorithms can be an effective tool for early diagnosis and intervention, which can help prevent heart failure in patients with atrial fibrillation.
Read Further At:
The Intelligent Future Of Healthcare: A Guide To Creating Bulletproof Digital Health Ecosystems
Integrating Augmented Artificial Intelligence With Physician Decision-Making: More Precise Cardiovascular Healthcare
Using Artificial Intelligence To Evaluate Frailty And Atrial Fibrillation
Artificial Intelligence And Machine Learning Algorithms In Prediction Of Atrial Fibrillation
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