Integrating Machine Learning and Big Data Analytics for Predictive Healthcare Outcomes

Abstract

The rapid growth of healthcare data, driven by electronic health records (EHRs), medical imaging, genomics, and wearable devices, has created new opportunities for predictive analytics in healthcare. Integrating machine learning (ML) with big data analytics enables the extraction of meaningful patterns from complex and high-dimensional datasets, supporting early disease detection, personalized treatment, and improved clinical decision-making. This study examines the role of ML techniques combined with big data infrastructures in predicting healthcare outcomes, highlighting key applications, methodological frameworks, benefits, and challenges. The paper emphasizes how predictive healthcare systems can enhance patient outcomes, optimize resource utilization, and support evidence-based medical practices, particularly in developing countries such as Pakistan.