CardiaTics stacked ensemble model with feature engineering improved heart disease prediction accuracy from 89.4% to 93.3%, outperforming individual classifiers.
Does the CardiaTics stacked ensemble model improve the accuracy of heart disease diagnosis compared to individual classifiers?
The CardiaTics stacked ensemble machine learning model, enhanced with feature engineering and explainable AI, achieves 93.3% accuracy in diagnosing heart disease while maintaining interpretability.
Effect estimate: Accuracy increase from 89.4% to 93.3% after feature selection with CardiaTics compared to individual classifiers
Absolute Event Rate: 93.3% vs 89.4%
Heart disease is a leading global cause of morbidity and mortality. Accurate and prompt diagnoses are crucial for its effective prevention and management. Integrating multiple machine learning algorithms, this research introduces a stacked ensemble machine learning model, called CardiaTics (stands for Cardiac DiagnosTics), toward improving heart disease detection. We detect outliers and remove them as a first-step to ensure data quality and maintain integrity. Ten distinct machine learning algorithms are then individually applied, culminating in the creation of a stacked ensemble model. We use feature engineering to refine the model further applying three well-known techniques –Pearson correlation, Chi-Square Test (Chi-2), and Recursive Feature Elimination. The implementation of these techniques on the benchmark dataset results in an optimized feature set. Experimental results show that CardiaTics delivers 89.3% accuracy on raw data, and significantly improves its accuracy after feature selection to 93.3%, outperforming the individual classifiers. However, can human professionals rely on algorithms for prediction when the underlying process is not fully understood? To address concerns regarding interpretability, trust, and transparency in black-box predictions, we propose utilizing SHapley Additive exPlanations (SHAP) and Explain Like I’m 5 (ELI5) in the second phase to elucidate feature importance in our model. The SHAP summary plots of CardiaTics reveal that the positive and negative contributors to heart disease are comparable, thereby enhancing the model’s interpretability and reliability and helping refine the decision-making process.
Ghose et al. (2026) studied Individuals with suspected heart disease aged 30-77 from the UCI Machine Learning Repository dataset (n=1,190). CardiaTics stacked ensemble machine learning model with feature engineering using Pearson correlation, Chi-Square Test, and Recursive Feature Elimination (RFE) vs. Individual machine learning classifiers (e.g., Random Forest, MLP, KNN, ETC, XGB, SVC, SGD, ABC, CART, GBM) was evaluated on Accuracy of heart disease prediction (Accuracy increase from 89.4% to 93.3% after feature selection with CardiaTics compared to individual classifiers). CardiaTics stacked ensemble model with feature engineering improved heart disease prediction accuracy from 89.4% to 93.3%, outperforming individual classifiers.