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.
Heart failure prediction is a critical task in healthcare analytics, enabling early diagnosis and timely intervention to reduce mortality rates. However, traditional clinical approaches often lack scalability and struggle to capture complex nonlinear relationships in patient data. To address these limitations, a machin...
Heart disease is a leading cause of mortality worldwide, with early detection playing a critical role inreducing death rates. Accurate prediction of heart disease remains challenging due to complex medical data andthe inability to provide continuous monitoring. Utilizing the Heart Disease dataset, various feature selec...
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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