Data Mining Applications for Enhancing Healthcare Services: A Comprehensive Review

The healthcare industry is experiencing a data-driven transformation, marked by the prolific generation of electronic health records (EHRs) and patient-related data. This paper delves into the potent realm of data mining applications within the healthcare environment, illustrating its capacity to revolutionize healthcare services. The extensive review explores data preprocessing techniques essential for enhancing data quality and reliability. It explores predictive modeling techniques, such as logistic regression, decision trees, and support vector machines, which empower healthcare professionals to predict disease risks, patient readmission rates, and medication adherence with precision. Furthermore, the paper elucidates the utility of clustering and classification techniques in devising personalized treatment regimens. Association rule mining is presented as a powerful tool for revealing concealed relationships amidst healthcare data, including symptom co-occurrence, drug interactions, and disease patterns. In practice, data mining serves as the bedrock for Clinical Decision Support Systems (CDSS), driving evidence-based healthcare decisions and recommendations. The applications extend to disease surveillance and outbreak detection, offering early warning systems that can trigger timely public health interventions. Data mining's capacity to unravel medication adherence challenges is showcased, thereby optimizing patient compliance. Additionally, healthcare fraud detection benefits from data mining's ability to uncover anomalous billing patterns. The paper concludes by addressing challenges like data privacy, source integration, and ethical considerations, while also highlighting the promising future of data mining in the realm of personalized medicine. As healthcare continues to digitize and data sources proliferate, harnessing data mining's capabilities is pivotal in advancing healthcare services, improving patient outcomes, and managing costs effectively.