Improving Diabetes Prediction Using a Hybrid Approach Based on Community Detection and Ontological Reasoning

Accurate prediction of Type 2 Diabetes is essential for effective prevention and intervention strategies. This paper proposes a hybrid model that integrates an improved Louvain community detection algorithm with a domain-specific diabetes ontology to enhance prediction performance and interpretability. Three real-world datasets (Pima Indians, Diabetes 130-US Hospitals, and NHANES) were used for evaluation. Patients were grouped into clinically meaningful clusters using the enhanced Louvain method, followed by semantic reasoning over an ontology constructed from expert-defined medical rules and clinical features. Experimental results show that the proposed model outperforms conventional clustering and classification techniques across multiple metrics, achieving up to 97.56% accuracy in cross-validation. The use of ontological knowledge not only increases transparency in prediction outcomes but also supports semantic querying and dynamic model adaptation. This method demonstrates significant potential for real-world deployment in clinical decision support systems. DOI: 10.61416/ceai.v28i1.9701