Enhancing Cardiac Health Diagnoses Through Machine Learning Analysis of Phonocardiograms (PCG)
Bibliographic record
Abstract
Phonocardiograms (PCG) provide a non-invasive approach to analyzing heart sounds, making them vital for the early detection of cardiac issues. However, identifying the most effective machine learning models and feature extraction techniques for classifying PCG signals remains a challenge. This study aims to determine the most efficient and accurate combinations of machine learning models and feature engineering techniques for classifying PCG signals, with the overarching goal of enhancing diagnostic capabilities in heart health. Seven machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Naive Bayes, AdaBoost, XGBoost, and Support Vector Machine (SVM)—were evaluated. Feature extraction methods such as Mel-frequency cepstral coefficients (MFCC), Linear Predictive Coding (LPC), and Short-Time Fourier Transform (STFT) were applied. Model performance was assessed using metrics including accuracy, precision, recall, and F1-score. The study found that advanced models like XGBoost and Random Forest, particularly when combined with STFT and MFCC features, consistently outperformed others. These models demonstrated superior accuracy and F1-scores, although they also introduced higher computational complexity. The results suggest that sophisticated model-feature combinations, particularly involving XGBoost and Random Forest with STFT and MFCC, hold promise for improving cardiac diagnostics. Received: 5 July 2024 | Revised: 18 October 2024 | Accepted: 23 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Popal Khan Popalzai: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation. Khurram Shehzad Khattak: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision. Anwar Mehmood Sohail: Conceptualization, Writing – original draft. Zawar Hussain Khan: Methodology, Supervision.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".