Paper Contents
Abstract
Predictive analytics in healthcare using machine learning has emerged as a transformative approach to improve patient outcomes, optimize treatment plans, and reduce costs. This study highlights the application of machine learning algorithms for predicting disease risks, patient readmissions, and treatment responses. Data from electronic health records (EHR), medical imaging, and wearable devices serve as input to train models such as decision trees, random forests, neural networks, and support vector machines. Our analysis demonstrates that predictive models can significantly enhance clinical decision-making, enabling early detection of diseases such as cancer, diabetes, and cardiovascular conditions. This research emphasizes the potential of predictive analytics to revolutionize healthcare delivery while addressing challenges such as data privacy, model interpretability, and ethical concerns. Furthermore, it discusses real-world implementations across hospital systems, telemedicine platforms, and population health monitoring, demonstrating how predictive analytics can reduce hospital readmissions by over 20% and improve survival outcomes in cancer treatment by enabling timely interventions. Machine learning (ML) modelsincluding decision trees, support vector machines, random forests, gradient boosting, and deep neural networksare increasingly being applied across domains such as cardiology, oncology, neurology, and intensive care units (ICUs). Keywords: Predictive analytics, healthcare, machine learning, disease prediction, EHR, clinical decision-making
Copyright
Copyright © 2025 S. NAVEEN PRASANTH. This is an open access article distributed under the Creative Commons Attribution License.
