Automatic Classification of Lung Sounds Using Machine Learning Algorithms
Bibliographic record
Abstract
Lung sounds provide substantial information about the state of respiratory system. These sounds are frequently influenced by noise from heart and muscles which complicate accurate diagnosis. This research concerns development of an efficient framework for automatically classifying lung auscultation sounds. Two well-known publically available lung sound datasets are utilized in this work. A total of 280 lung sounds of a varying duration of 3 seconds to 1 minute with sampling rates of 4k, 10k, and 44.1k Hz were used. The raw signals were first pre-processed by resampling to 4 kHz and zero-padding for uniformity and fixed-length duration, and then segmented. Next, Mel-Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) were computed. Then 3,299,341 combined extracted features were used to train (70%) and validate (30%) models including Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The best results were obtained with STFT+MFCC-ANN combination with an accuracy of 98.61%, 98% F1-score, 98% recall, and 99% precision.
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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".