The Role of Data Mining in Modern Healthcare: A Review of Predictive Models, Descriptive Techniques, and Emerging Trends

Abstract

The advancements of technology and the wide implementation of Electronic Health Records (EHRs) have resulted in an unprecedented data size in the healthcare industry. While these massive, complex, and heterogenous datasets hold significant potential for improving clinical decision-making, extracting meaningful knowledge from high-dimensional and heterogeneous medical data remains a significant obstacle for data-handling mechanisms. Data mining has emerged as an essential methodology within Knowledge Discovery in Databases (KDD) to uncover hidden clinical patterns, anomalies, and correlations. This paper provides a comprehensive survey and deep methodological overview of the role of data mining in medicine. 21 recent studies conducted between 2019 and 2025 critically evaluated a diverse range of single and hybrid data mining architectures, including classification, clustering, and deep learning algorithms applied across various medical subfields (such as oncology, neurology, and chronic disease prediction).  This analysis revealed that while hybrid data mining models and ensemble techniques demonstrate superior predictive performance, constantly achieving classification accuracies exceeding 95%, the existing literature shows critical gaps. Many current frameworks depend on theoretical or un-validated models that fail to address real-world implementation challenges, i.e., parameter-tuning sensitivities, high computational complexities, and potential overfitting due to small or imbalanced datasets.  The significance of this work lies in identifying these systemic architectural and data-structural bottlenecks to facilitate a steady transition from theoretical data mining models to practical, deployable systems in clinical settings. Finally, this paper illustrated upcoming trends and Artificial Intelligence (AI) integration, providing a roadmap for future research aimed at enhancing the robustness and scalability of healthcare analytics.