TL;DR
A hybrid Multi-Layer Perceptron (MLP)–XGBoost model for heart disease prediction is proposed, combining the feature extraction capability of neural networks with the ensemble robustness of gradient boosting to enhance both predictive accuracy and reliability for cardiovascular disease diagnosis.
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 demonstrate that the optimized XGBoost-SMOTE model significantly outperforms traditional machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, KNearest Neighbors, AdaBoost, and baseline XGBoost.
A robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component using an Artificial Neural Network employed as a feature extraction layer prior to ensemble aggregation is presented.
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 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.
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