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Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A
1Department of Medicine, Division of Cardiology, University of British Columbia, 9th Floor 2775 Laurel St., Vancouver, BC V5Z 1M9, Canada.
Insights
Machine learning (ML) algorithms show promise in detecting left ventricular hypertrophy (LVH) using ECGs, often improving sensitivity but not consistently outperforming traditional criteria in specificity. Further standardization is needed for clinical application.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Left ventricular hypertrophy (LVH) is a significant risk factor for cardiovascular events.
- Early and accurate identification of LVH is crucial for patient outcomes.
- Traditional electrocardiogram (ECG) criteria have limitations in diagnosing LVH.
Purpose of the Study:
- To systematically review and compare machine learning (ML) algorithms for LVH detection using ECG.
- To evaluate ML algorithms against classical ECG criteria regarding sensitivity, specificity, and accuracy.
- To assess the clinical utility of ML algorithms in identifying LVH.
Main Methods:
- Systematic literature search of Medline and PubMed using keywords related to LVH, ECG, and ML.
- Inclusion of 14 studies employing various ML algorithms (SVM, Random Forest, Neural Networks, etc.).
- Comparison of ML algorithm performance metrics (sensitivity, specificity, accuracy, ROC) with traditional ECG criteria.
Main Results:
- ML algorithms demonstrated a wide range of sensitivity (0.29-0.966) and specificity (0.53-0.99).
- ML approaches generally showed higher sensitivity than classical ECG criteria (Cornell, Sokolow-Lyons).
- Specificity of ML algorithms was not consistently superior, with some performing worse than traditional methods; many utilized extensive clinical and ECG data, limiting practical screening use.
Conclusions:
- Numerous ML algorithms exist for LVH detection via 12-lead ECG, incorporating diverse signal analyses and clinical data.
- While many ML algorithms enhance sensitivity, they often fail to surpass the specificity of established ECG criteria.
- Standardized databases are essential for robust comparison and validation of ML algorithms for LVH detection.
Abstract:
Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators "left ventricular hypertrophy, electrocardiogram" AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.
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