ArticleBioengineering (Basel, Switzerland)2022
An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 71 citations in OpenAlex.
- A Synchronous Triggering Method for Impact Artificial Seismic Source and Seismographs Based on Non-Contact Audio Detection.Sensors (Basel, Switzerland) · 2026Article
- Advancing cardiovascular screening: deep learning-based heart-sound classification using SMOTE and temporal modeling.Scientific reports · 2026Article
- HSD-Net: a dual-branch CNN-BiLSTM network with hybrid cepstral fusion for heart sound classification.Frontiers in medical technology · 2026Article
- Role of urinary leukocytes in the risk stratification of prostate cancer using nonlinear stacking learning strategy: a bi-cohort diagnostic study.Frontiers in oncology · 2026Article
- Heart Sound Classification with MFCCs and Wavelet Daubechies Analysis Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning for preventing stillbirths: is it possible to transform data into life-saving insights?BMC pregnancy and childbirth · 2025Article
- Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis.Scientific reports · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- Article
- Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.Scientific reports · 2025Article
- A wearable approach for Sarcopenia diagnosis using stimulated muscle contraction signal.Biomedical engineering letters · 2025Article
- Advanced heart disease classification based on multi-channel heart sound coupling features.PloS one · 2025Article
- Article
- Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.Health information science and systems · 2024Article
- Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation.Scientific reports · 2024Article
- PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.Diagnostics (Basel, Switzerland) · 2023Article
- An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms.Scientific reports · 2023Article
- Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician's skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.