Detecting Arrhythmias Using ECG Signals with Machine Learning Techniques
Bibliographic record
Abstract
This paper presents a comparative study on the effectiveness of various feature selection methods and machine learning algorithms for arrhythmia prediction using electrocardiogram (ECG) signals. ECGs are crucial for diagnosing abnormal heart rhythms, known as arrhythmias, which arise from changes in the heart’s electrical impulse patterns. The study focuses on two primary feature selection techniques: the Sequential Feature Selection (a wrapper method) and the Pearson Correlation (a filter method). These methods were applied to a real-world medical dataset to assess the impact on machine learning model performance. Our approach involved data training and testing through methods such as Cross-Validation Partition and an 80-20 training-testing split, iterating through the methods to find optimal conditions. A number of machine learning models were tested, including Naive Bayes Classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Ensemble Methods. Initial tests used a generic Naive Bayes implementation, which was later evaluated against reduced feature sets generated by Sequential Feature Selection and Pearson Correlation. The performance of these models was measured using metrics such as accuracy, balanced accuracy, sensitivity, and specificity. Results showed that the Naive Bayes Classifier achieved the highest accuracy, and both Naive Bayes and SVM exhibited high specificity at 94%. This study underscores the value of targeted feature selection in enhancing the predictive accuracy and efficiency of machine learning models in medical diagnostics, particularly in predicting arrhythmias from ECG data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".