ECG Multi Class Classification Using Machine Learning Techniques
Bibliographic record
Abstract
This paper presents a project focused on utilizing Artificial Intelligence (AI) tools to improve the process of diagnosing heart diseases. The research indicates that 10 to 15 percent of Pacific Islanders are diagnosed with at least one form of heart disease, leading to around 20,000 deaths annually. The proposed project uses the Physio net database and ECG signals of 162 patients to design a multi-class classification method that accurately recognizes different patterns under 3 classes, namely, Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The study utilizes two feature extraction methods, Continuous Wavelet Transform, and Wavelet Scattering, to extract the principal characteristics from the ECG data. MATLAB Software is used to train three models, an AlexNet Model, an SVM Model, and an LSTM Model, to diagnose cardiovascular diseases and their severity. The results of the different classification methods showed that the SVM Model had the best performance with a classification accuracy of 98%. This project offers a dependable and effective diagnostic tool for the diagnosis of heart diseases with a minimized risk of human error. Additionally, it has the potential to serve as a valuable resource for future studies in the medical field aimed at enhancing cardiovascular disease diagnosis and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".