article · Engineering Technology & Applied Science Research
This study addresses the critical need for accurate and early diagnosis of Cardiovascular Disease, a major cause of global mortality. It introduces an advanced Ensemble Learning framework designed for robust heart disease detection and classification. The framework integrates an Artificial Neural Network for initial feature extraction, which then feeds into an ensemble of machine learning algorithms, including Random Forest, XGBoost, and LightGBM. Evaluated on a benchmark dataset, the proposed model achieved a high accuracy of 98.8%, demonstrating superior performance compared to individual classifiers and existing methods. The research highlights that the ANN-based feature extraction significantly enhances the model's generalisation capabilities and reduces prediction errors, making it effective for early detection and clinical decision support.
Cardiovascular disease is a major global health concern. This research offers a highly accurate method for early detection, which could significantly improve patient outcomes by enabling timely medical intervention. Developing more reliable diagnostic tools is crucial for reducing mortality rates associated with heart conditions.
This early-stage research presents a robust framework for heart disease detection, which could be integrated into clinical decision support systems. Potential users include healthcare professionals, such as cardiologists and general practitioners, who require accurate diagnostic aids. The technology could assist in identifying at-risk patients earlier, potentially leading to improved treatment pathways and patient management.
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Cardiovascular Disease (CVD) remains one of the leading causes of mortality worldwide, emphasizing the need for accurate and early diagnostic solutions. Recent advances in Machine Learning (ML) and Deep Learning (DL) have shown significant potential to support clinical decision-making through data-driven prediction models. This study presents a robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component. Specifically, an Artificial Neural Network (ANN) is employed as a feature extraction layer prior to ensemble aggregation using techniques such as Random Forest, XGBoost, and LightGBM. The proposed approach is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results on a benchmark dataset demonstrate that the model achieves a high accuracy of 98.8%, outperforming individual classifiers and existing approaches. The integration of ANN-based feature extraction enhances model generalization and reduces prediction error. These findings highlight the effectiveness of the proposed framework for early heart disease detection and clinical decision support.
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DOI: 10.48084/etasr.19612
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