ArticleScientific reports2024
Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Sustainable and interpretable heart disease prediction: a clinical decision support approach for biomedical healthcare applications.Scientific reports · 2026Article
- Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification.Frontiers in physiology · 2026Article
- An explainable hybrid framework for early detection of cardiovascular diseases using Categorical Boosting and Bees algorithm.Scientific reports · 2025Article
- PSO-BiLSTM-Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing cardiac disease prediction with explainable bidirectional LSTM.Scientific reports · 2025Article
- Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.Scientific reports · 2025Article
- Cognitive difference text classification in online knowledge collaboration based on SA-BiLSTM hybrid model.Scientific reports · 2025Article
- Article
- An Enhanced Hybrid Model Combining CNN, BiLSTM, and Attention Mechanism for ECG Segment Classification.Biomedical engineering and computational biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model performance may lag due to the unavailability of annotated datasets. To overcome these issues, the research proposed a model for heart disease detection and classification by integrating blockchain technology with a Modified mixed attention-enabled search optimizer-based CNN-Bidirectional Long Short-Term Memory (M2MASC enabled CNN-BiLSTM) model. The novel model incorporates a pre-trained VGG16 model to enhance feature extraction and improve the overall predictive accuracy. Leveraging the continuous monitoring capabilities of IoT devices, patient data is collected in real-time, providing a dynamic source to the CNN-BiLSTM model. Blockchain integration ensures stored health data's security, transparency, and immutability, addresses privacy concerns, and promotes trust in the predictive system. The classifier parameters are tuned using the modified mixed attention and search optimization. The M2MASC-enabled CNN-BiLSTM model performs better than traditional methods of accuracy 98.25%, precision 99.57%, and recall 97.53% for TP 80 with the MIT-BIH dataset.
Indexed as
Identifiers
What OpenQuestion holds
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.