A smarter forest: Enhancing cardiovascular risk prediction using a knowledge-based random forest - Chanmee, Sirichanya; Kesorn, Kraisak (Science, Engineering and Health Studies, 2025)
Predicting heart disease and other cardiovascular issues accurately is critical for enabling early intervention and improving patient outcomes. This study proposed the semantic random forest (SRF) framework, which enhances the classification performance of conventional random forest (RF) algorithms for heart disease prediction. The conventional RF framework is augmented through the integration of knowledge from a formal ontology model that encapsulates domain-specific medical knowledge, thereby providing a structured representation of concepts, relationships, and axioms. The SRF framework utilizes this ontology during the classification process to yield more precise predictions. The effectiveness of the proposed SRF framework was evaluated against the conventional RF, AdaBoost, and gradient boosting algorithms, with a focus on their ability to classify heart disease instances accurately. Experimental results demonstrate that the proposed SRF framework outperformed the baseline algorithms on two datasets, achieving the highest accuracy and Matthews correlation coefficient values of 0.8296 and 0.6589 on the University of California at Irvine dataset and 0.9856 and 0.9706 on Mendeley dataset, respectively. The results demonstrate that ontology-based structured knowledge significantly improves the classification power of traditional RF algorithms, which highlights this knowledge-driven approach’s potential to predict heart disease risks in computer-aided medical diagnoses.