Acentric artificial intelligence with deep feature engineering for early heart disease risk prediction

Abstract

Early identification of heart disease is important to reduce mortality rates and to provide timely medical intervention for better patient outcomes. In recent studies, machine learning has been used to predict cardiovascular risk, but many existing models use basic feature sets and fixed decision rules. This can limit their ability to adapt when new data are introduced and may also reduce their capability to detect early signs of risk. In this study, we present a heart disease prediction framework that integrates acentric artificial intelligence (Ac-AI) with deep feature engineering to improve prediction accuracy. We adopted an autoencoder-based representation learning module to learn compact latent features from clinical data. These learned features were then combined with the original variables to provide a more informative set of inputs for subsequent analyses. The Ac-AI classifier applies cost-sensitive learning and an adaptive decision threshold to improve sensitivity for disease classes. Across multiple experiments conducted on a benchmark heart disease dataset, the proposed model showed performance comparable to that of random forest (RF), support vector machine (SVM), and XGBoost. In some runs, it performed better than these baseline models. Repeated independent runs provided evidence that the method produced consistent results across trials. These results suggest that Ac-AI combined with deep feature engineering can serve as a useful decision support framework for the early prediction of heart disease risk.