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Published on: September 26, 2018
A novel transformer-based approach for cardiovascular disease detection
Nimra Noor1, Muhammad Bilal1, Saadullah Farooq Abbasi2
1Department of Artificial Intelligence, Rare Sense Inc, Covina, CA, United States.
Insights
A new transformer algorithm accurately classifies cardiovascular diseases (CVDs) using electrocardiography data. This efficient, low-cost method shows high performance, aiding early CVD prediction and reducing mortality.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Cardiovascular diseases (CVDs) are a leading cause of global mortality, accounting for 17.9 million deaths annually.
- Early detection of CVDs, including arrhythmia and heart failure, is crucial for reducing mortality rates.
- Existing diagnostic methods require improvement in efficiency and cost-effectiveness for widespread application.
Purpose of the Study:
- To develop a novel, efficient, and low-cost transformer-based algorithm for the classification of cardiovascular diseases.
- To enhance the early prediction capabilities for various CVDs using electrocardiography (ECG) data.
- To evaluate the performance of the proposed algorithm against existing state-of-the-art methods.
Main Methods:
- Extraction of 56 features from 1,200 ECG records representing four distinct cardiovascular diseases.
- Application of Random Forest for feature selection, identifying the 13 most significant features.
- Development and implementation of a novel transformer-based algorithm for classifying the four CVD classes.
Main Results:
- The proposed transformer algorithm achieved exceptional classification performance.
- Maximum accuracy reached 0.9979, with precision, recall, and F1 scores all exceeding 0.9958.
- The algorithm demonstrated superior performance compared to all previously reported state-of-the-art CVD classification methods.
Conclusions:
- The developed transformer-based algorithm offers a highly accurate and efficient solution for cardiovascular disease classification.
- This novel approach holds significant potential for improving early CVD detection and patient outcomes.
- The low-cost and efficient nature of the algorithm makes it suitable for broader clinical implementation.
Abstract:
According to the World Health Organization, cardiovascular diseases (CVDs) account for an estimated 17.9 million deaths annually. CVDs refer to disorders of the heart and blood vessels such as arrhythmia, atrial fibrillation, congestive heart failure, and normal sinus rhythm. Early prediction of these diseases can significantly reduce the number of annual deaths. This study proposes a novel, efficient, and low-cost transformer-based algorithm for CVD classification. Initially, 56 features were extracted from electrocardiography recordings using 1,200 cardiac ailment records, with each of the four diseases represented by 300 records. Then, random forest was used to select the 13 most prominent features. Finally, a novel transformer-based algorithm has been developed to classify four classes of cardiovascular diseases. The proposed study achieved a maximum accuracy, precision, recall, and F1 score of 0.9979, 0.9959, 0.9958, and 0.9959, respectively. The proposed algorithm outperformed all the existing state-of-the-art algorithms for CVD classification.
