ArticleScientific reports2025
Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Uncertainty-aware feature-weighted ensemble framework for heart disease prediction.Scientific reports · 2026Article
- Optimizing PACU nursing resource allocation through SARIMA-based patient volume forecasting: a case study from a tertiary hospital in China (2020-2021).BMC health services research · 2026Article
- A comparative performance analysis of ensemble learning and regularized neural networks in cardiovascular risk prediction.Frontiers in medical technology · 2026Article
- Machine learning prediction of long-term sickness absence due to mental disorders using Brief Job Stress Questionnaire data.Scientific reports · 2025Article
- Software effort estimation based on inception network optimized by enhanced banyan tree growth optimizer.Scientific reports · 2025Article
- A hybrid bio inspired neural model based on Ropalidia Marginata behavior for multi disease classification.Scientific reports · 2025Article
- Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes.BMC public health · 2025Article
- Bioinformatics analysis of IFI6 as a novel prognostic biomarker and its correlation with immune infiltration in breast cancer.Scientific reports · 2025Article
- Non-technical loss detection in power distribution networks using machine learning.Scientific reports · 2025Article
- Machine learning framework for predicting susceptibility to obesity.Scientific reports · 2025Article
- COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search.Scientific reports · 2025Article
- Application of machine learning models for predicting depression among older adults with non-communicable diseases in India.Scientific reports · 2025Article
- Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms.Plant methods · 2025Article
- An enhancement of machine learning model performance in disease prediction with synthetic data generation.Scientific reports · 2025Article
- Machine learning model to predict mortality in patients with skin and soft tissue infection in emergency department.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Deep transfer learning and attention based P2.5 forecasting in Delhi using a decade of winter season data.Scientific reports · 2025Article
- NSPLformer: exploration of non-stationary progressively learning model for time series prediction.Scientific reports · 2025Article
- Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients.BMC medical informatics and decision making · 2025Article
- DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.Scientific reports · 2025Article
- Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS.Scientific reports · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart disease is a category of various conditions that affect the heart, which includes multiple diseases that influence its structure and operation. Such conditions may consist of coronary artery disease, which is characterized by the narrowing or clotting of the arteries that supply blood to the heart muscle, with the resulting threat of heart attacks. Heart rhythm disorders (arrhythmias), heart valve problems, congenital heart defects present at birth, and heart muscle disorders (cardiomyopathies) are other types of heart disease. The objective of this work is to introduce the Greylag Goose Optimization (GGO) algorithm, which seeks to improve the accuracy of heart disease classification. GGO algorithm's binary format is specifically intended to choose the most effective set of features that can improve classification accuracy when compared to six other binary optimization algorithms. The bGGO algorithm is the most effective optimization algorithm for selecting the optimal features to enhance classification accuracy. The classification phase utilizes many classifiers, the findings indicated that the Long Short-Term Memory (LSTM) emerged as the most effective classifier, achieving an accuracy rate of 91.79%. The hyperparameter of the LSTM model is tuned using GGO, and the outcome is compared to six alternative optimizers. The GGO with LSTM model obtained the highest performance, with an accuracy rate of 99.58%. The statistical analysis employed the Wilcoxon signed-rank test and ANOVA to assess the feature selection and classification outcomes. Furthermore, a set of visual representations of the results was provided to confirm the robustness and effectiveness of the proposed hybrid approach (GGO + LSTM).
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.