Computational detection and interpretation of heart disease based on conditional variational auto-encoder and stacked ensemble-learning framework

Worldwide, cardiovascular disease is the leading cause of death. Based on clinical data, a Machine Learning (ML) system can detect cardiac disease in its early stages, which enables a reduction in mortality rates. However, imbalanced and high dimensionality data have been a persistent challenge in ML, impeding accurate predictive data analysis in many real-world applications, such as the detection of cardiovascular disease. To address this, computational methods targeting heart disease detection have been developed. However, their performance is still inadequate. Hence, this study presents a new stack predictor for the heart disease model (termed SPFHD). SPFHD employs five common tree-based ensemble learning algorithms as base models for heart disease detection. In addition, the predictions from the base models are integrated using a support vector machine algorithm to enhance the accuracy of heart disease detection. A new conditional variational autoencoder (CVAE) based method is developed to overcome the imbalance issue, which performs better than the conventional balancing methods. Finally, the SPFHD model is tuned by Bayesian optimization. The results show that the proposed SPFHD model outperforms the state-of-art methods over four datasets achieving higher f1-score of 4.68 %, 4.55 %, 2 %, and 1 % for HD clinical, Z-Alizadeh Sani, Statlog, and Cleveland, respectively. Moreover, this new framework offers vital interpretations which assist in understanding model success by leveraging the powerful SHapley Additive explanation (SHAP) algorithm. This highlights the most significant attributes for detecting heart disease and overcoming the limitations of current 'Black-box' methods that cannot reveal causal relationships between features.