Recent healthcare applications of natural language processing involve multi-label classification of health records using the International Classification of Diseases (ICD). While prior research highlights intricate text models and explores external knowledge like hierarchical ICD ontology, fewer studies integrate code relationships from whole datasets to enhance ICD coding accuracy. This study presents a modular approach, sequentially combining graph-based integration of ICD code co-occurrence with a hard-coded hierarchical enriched text representation drawn from the ICD ontology. Findings reveal: 1) significant performance gains in the combined model, aside from the significant performance gain in each enhancement module in isolation, 2) graph-based module’s efficacy is more pronounced when applied to enhanced features using the hierarchical ICD ontology, and 3) experiments demonstrate hierarchy depth’s impact on performance, concluding the deepest level’s enrichment.