Advanced Cardiovascular Disease Prediction: A Comparative Analysis of Ensemble Stacking and Deep Neural Networks
Keywords:
machine learning, cardio disease, ensemble learning, XGB, deep networksAbstract
Advancements in computational powers and approaches have significantly diversified the field of medical sciences, particularly in diagnosing cardiovascular problems in humans. Cardiovascular disease (CVD) is a complex and demanding ailment that has a substantial negative influence on the worldwide population, substantially affecting human health and quality of life. Early and accurate identification of cardiovascular disorders in individuals can offer significant benefits in treating heart failure in its early stages, hence improving the chances of the patient's survival. The manual detection of cardiac disease is susceptible to bias and subject to discrepancies among examiners. Machine learning algorithms have shown effectiveness and dependability in accurately detecting and categorizing individuals with heart disease and those in a normal state of health. This research paper presents a novel method using an ensemble deep neural network to detect heart disease precisely. Our approach integrates two deep learning networks as fundamental models, together with an XGBoost boosting model acting as the meta-classifier to generate ultimate predictions. The experimental results demonstrate that our ensemble model surpasses the existing methodologies documented in the literature, showcasing its higher efficacy in detecting cardiac disease. This novel technique shows potential for improving the precision and dependability of heart illness detection, thereby benefiting clinical practice and patient care.
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