ArticleSensors (Basel, Switzerland)2022
Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- Machine learning approach for unmet medical needs among middle-aged adults in South Korea: a cross-sectional study.BMC health services research · 2025Article
- Predator crow search optimization with explainable AI for cardiac vascular disease classification.Scientific reports · 2025Article
- HeartEnsembleNet: An Innovative Hybrid Ensemble Learning Approach for Cardiovascular Risk Prediction.Healthcare (Basel, Switzerland) · 2025Article
- [Research on intelligent fetal heart monitoring model based on deep active learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique.Journal of orthopaedic surgery and research · 2024Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.Scientific reports · 2024Article
- A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.BMC public health · 2024Article
- A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.Frontiers in artificial intelligence · 2024Article
- Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.Journal of diabetes research · 2024Article
- Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.PeerJ. Computer science · 2024Article
- Machine Learning and Single-Cell Analysis Identify Molecular Features of IPF-Associated Fibroblast Subtypes and Their Implications on IPF Prognosis.International journal of molecular sciences · 2023Article
- Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum.Journal of medical Internet research · 2023Article
- Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023Article
- Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.Diagnostics (Basel, Switzerland) · 2023Article
- Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest.Scientific reports · 2023Article
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
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4 authors.
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Abstract
The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user-expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.
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