Integrating Machine Learning and Deep Learning for Multiclass Mortality Prediction in Healthcare Data
Abstract
The prediction of mortality in cardiovascular patients demands models that balance accuracy, interpretability, and robustness. The current research work implementation presents a unified system that integrates ensemble machine learning (ML) approaches with complex deep learning (DL) architectures for multiclass mortality prediction. The ML component employs Random Forest, Gradient Boosting, and XGBoost classifiers, each evaluated through stratified sampling, confusion matrices, and precision–recall analysis. These models establish strong baselines, with XGBoost achieving the highest accuracy among the ensemble group. Building upon this foundation, the DL component introduces fully connected neural networks (FCNNs) enhanced with dropout and batch normalization, convolutional neural networks (CNNs) adapted for tabular data, and an FCNN augmented with attention mechanisms to capture feature importance. The CNN model demonstrated superior generalization, attaining validation accuracy above 96%, while the attention‑based FCNN provided interpretability without compromising predictive strength. Comparative visualization of accuracy curves and error distributions underscores the complementary strengths of ML and DL approaches. This hybrid pipeline not only advances methodological rigor but also contributes to reproducible AI practices in healthcare, offering a scalable result for clinical choice validation in mortality risk assessment.
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