LEVERAGING KNOWLEDGE GRAPHS WITH MACHINE LEARNING FOR HEART DISEASE PREDICTION

Heart disease remains a leading cause of morbidity and mortality worldwide, and early detection is crucial to reducing its impact on public health. Traditional methods of heart disease prediction rely on structured clinical data such as blood pressure, cholesterol levels, and patient demographics. While these methods offer valuable insights, they often fail to capture the complex relationships between various risk factors, genetic predispositions, lifestyle choices, and medical histories that contribute to heart disease. To address these limitations, this paper explores the integration of Knowledge Graphs (KGs) with machine learning (ML) techniques as a novel approach to heart disease prediction.Knowledge graphs are a type of data representation that models’ entities (e.g., diseases, patients, symptoms, treatments) as nodes and captures relationships between them through edges. These graphs can represent complex, multi-dimensional medical knowledge, which is particularly useful for understanding how various health conditions and risk factors interact. By leveraging the power of KGs, this paper demonstrates how a richer, more holistic understanding of patient data can be incorporated into predictive models for heart disease. The ability of KGs to integrate data from diverse sources such as electronic health records (EHR), clinical studies, medical literature, and genomic databases presents a unique opportunity to improve the predictive accuracy of heart disease models.Machine learning algorithms, particularly those utilizing graph-based techniques like Graph Neural Networks (GNNs), have shown promise in learning from graph-structured data. These algorithms can capture complex dependencies and infer new relationships that traditional machine learning models may overlook. The synergy between KGs and ML allows the development of models that not only predict the likelihood of heart disease but also provide insights into the underlying causes and risk factors. These models can also enhance clinical decision-making by offering explainable and transparent predictions, making them more trustworthy for healthcare professionals.