Hybrid Multi-Model Fuzzy Ensemble Approach for Cardiovascular Diseases Detection

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dc.contributor.authorChugh, Manop
dc.contributor.authorAnantavrasilp, Isara
dc.contributor.authorThiemjarus, Surapa
dc.date.accessioned2026-08-06T10:39:40Z
dc.date.available2026-08-06T10:39:40Z
dc.date.issued2023-01-01
dc.description.abstractTimely detection of cardiovascular diseases (CVDs) is crucial to reducing mortality rates. Recent advances in artificial intelligence (AI) and machine learning (ML) models for CVD detection often suffer from low model performance and hence lower accuracy and practicality of early CVD detection. In this study, we propose a novel hybrid ensemble learning framework that combines multiple ML algorithms and a fuzzy expert system to improve CVD diagnosis and prediction accuracy. We evaluate our proposed method on two standard datasets, namely the UCI Cleveland and Framingham, and compare it with four popular ensemble algorithms, namely Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and Adaptive Boosting. Our results demonstrate that the proposed ensemble learning framework achieves higher accuracies of 91.2% (UCI Cleveland) and 91.7% (Framingham), surpassing existing algorithms by 3.3% and 8.8%, respectively.
dc.identifier.citation2023 IEEE World AI Iot Congress Aiiot 2023, 454-459, 2023
dc.identifier.doi10.1109/AIIoT58121.2023.10174458
dc.identifier.other2-s2.0-85166651523
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13924
dc.source2023 IEEE World AI Iot Congress Aiiot 2023
dc.subjectcardiovascular disease
dc.subjectensemble learning
dc.subjectfuzzy logic
dc.subjecthybrid intelligent systems
dc.subjectmachine learning
dc.titleHybrid Multi-Model Fuzzy Ensemble Approach for Cardiovascular Diseases Detection
dc.typeConference Paper

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