The rapid advancement of predictive modeling techniques has revolutionized early detection and management of chronic diseases. This chapter explores the integration of multi-modal health data and the application of advanced computational methods to enhance predictive accuracy in chronic disease prediction. By leveraging diverse data sources, including EHRs, wearable devices, genomics, and imaging, multi-modal models offer a comprehensive understanding of patient health, enabling early identification of disease risk. Emphasis was placed on the use of machine learning, graph-based models, and probabilistic approaches to capture complex interdependencies within heterogeneous data streams. Challenges related to data preprocessing, semantic interoperability, and bias mitigation in predictive systems are critically examined. The chapter also highlights the role of explainability in ensuring transparency and fairness, ensuring that predictive models are both clinically effective and ethically sound. Future directions for the integration of cutting-edge technologies such as federated learning and edge computing are also discussed, alongside their potential to transform population health monitoring and chronic disease management.ÂÂÂ
Chronic diseases remain a major cause of morbidity and mortality worldwide, placing a significant burden on healthcare systems and economies [1]. Conditions such as cardiovascular disease, diabetes, cancer, and respiratory disorders are often diagnosed at later stages, making treatment more difficult and less effective. Traditionally, disease detection and management have relied on episodic clinical visits, diagnostic tests, and subjective risk assessments [2]. These methods often fail to detect diseases at their earliest, most treatable stages, contributing to delayed interventions [3]. As healthcare systems increasingly adopt data-driven approaches, predictive modeling has emerged as a promising solution for the early detection and management of chronic diseases, offering the potential to identify individuals at risk long before symptoms appear [4]. In recent years, the integration of multi-modal health data has significantly enhanced the capabilities of predictive models [5]. Multi-modal data refers to the combination of diverse data types, such as EHRs, genomic information, medical imaging, and real-time data from wearable devices [6]. These varied sources of information provide a comprehensive understanding of a patient’s health status, capturing both physiological and behavioral aspects that traditional models often overlook [7]. The fusion of such heterogeneous data allows predictive models to consider a wider array of risk factors, leading to more accurate and individualized risk assessments [8]. The ability to leverage multi-modal data not only improves prediction accuracy but also provides clinicians with a more holistic view of patient health, enabling more personalized interventions and treatment plans [9].ÂÂÂ
