Enhancing Cardiovascular Disease Prediction: A Domain Knowledge-Based Feature Selection and Stacked Ensemble Machine Learning Approach

Abstract Cardiovascular diseases (CVDs) are prevalent disorders affecting the heart or blood arteries. Early disease detection significantly enhances survival prospects, thus emphasizing the necessity for accurate prediction methods. Emerging technologies, such as machine learning (ML), present promising avenues for more precise prediction of CVDs. However, a critical challenge lies in developing models that not only ensure optimal predictive performance but also conform to well-established domain knowledge, thereby enhancing their credibility. Single classifiers often fall short due to issues like overfitting and bias. In response, this study proposes a domain knowledge-based feature selection integrated with a stacking ensemble classifier. The Framingham Heart Study, UCI Heart Disease and UAE retrospective cohort study datasets were utilized for training and evaluation of the ML algorithms. The results indicate that the proposed domain knowledge-based feature selection performs on par with frequently adopted feature selection techniques. Moreover, the proposed stacked ensemble, in conjunction with domain knowledge-based feature selection, achieved the highest metrics with 89.66% accuracy, and 89.16% F1-score on the Framingham dataset. Similarly, the proposed method achieved an F1-score of 85.26% and 96.23% on the UCI Heart Disease and UAE datasets. Furthermore, this study employs explainable AI techniques to illuminate the decision-making process of the predictive models. Thus, the study establishes that domain knowledge-based feature selection promotes the credibility of ML models without compromising predictive performance.