TL;DR
This research proposes a tri-level ensemble approach that combines bagging, boosting, and stacking to classify various medical diseases and achieves an average accuracy of 97.00% on Pima, Yeast, Leukemia, Breast Cancer and Statlog datasets.
The research work concludes that ML models, when properly tuned and validated, can significantly assist in the early diagnosis of heart disease, offering critical support for clinical decision-making.
Experimental results show that the hybrid MVA based models outperforms the traditional classifiers in terms of prediction accuracy, computational efficiency and feature optimization.
The high performance highlights the effectiveness of advanced ensemble learning in achieving reliable heart disease predictions, emphasizing the potential of integrating robust feature selection with sophisticated classification models for precise medical data analysis.
An ensemble learning-based framework for improved heart disease prediction using multiple datasets and Explainable Artificial Intelligence (XAI) to evaluate model performance in terms of the clinical features that are most relevant in predicting heart disease.
A machine learning-driven framework developed using the healthcare-dataset-stroke-data, comprising diverse clinical attributes associated with cardiovascular conditions, improves both prediction accuracy and model transparency in heart failure classification.
The study comes to the conclusion that headline accuracy is an unreliable guide in imbalanced medical prediction, that imbalance handling can change a model's practical usefulness, and that this benefit is strongly algorithm-dependent, meaning that the decision to resample should be based on the algorithm and the scree...
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