Key result
The proposed Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer approach achieved an accuracy of 97.89% for cardiovascular disease detection, outperforming comparative methods.
Why the study?
Developing countries often lack CVD experts and face high rates of misdiagnosis, which could be reduced through an accurate and effective early-stage prediction system.
Does the GBDT-BSHO machine learning approach improve the accuracy of cardiovascular disease detection compared to other methods?
Does the GBDT-BSHO machine learning approach improve the accuracy of cardiovascular disease detection compared to other methods?
A novel machine learning approach combining Gradient Boosted Decision Trees with a Binary Spotted Hyena Optimizer achieved 97.89% accuracy in detecting cardiovascular disease in a standard dataset.
ML models may aid CVD prediction where experts are scarce; leaves open prospective validation before clinical adoption.
Cardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machine learning and data mining offer the potential for finding risk variables and predicting CVD. Developing countries often need more CVD experts, and a high percentage of misdiagnosis. These concerns could be alleviated using an accurate and effective early-stage heart disease prediction system. This study explores the effectiveness of machine learning classifiers for diagnosing and detecting CVD. Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. The Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) suggested in this work was used to rank and classify all attributes. Discrete optimization problems can be resolved using the binary form of SHO. The recommended method compresses the continuous location using a hyperbolic tangent function. The updated spotted hyena positions on the relevance score are utilized to find those with high heart disease predictions. The efficiency of the suggested model is then confirmed using the UCI dataset. The proposed GBDT-BSHO approach, with an accuracy of 97.89%, was significantly more effective than the comparative methods.
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Kiran et al. (2023) studied Cardiovascular disease. Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) vs. comparative methods was evaluated on Accuracy. The proposed Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer approach achieved an accuracy of 97.89% for cardiovascular disease detection, outperforming comparative methods.
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