Integrating Classic Optimization Methods With Machine Learning for Enhanced Predictive Analytics in Healthcare

Abstract

Using machine learning and conventional optimisation methods improves healthcare predictive analytics accuracy, interpretability, and clinical relevance. This study suggests combining machine learning's predictive power with optimization's decision-making rigour. Healthcare situations like patient readmission prediction and treatment planning use the paradigm. Incorporating limitations and objectives directly into prediction models ensures accurate, realistic, and clinically relevant outcomes. These strategies improve healthcare decision-making by addressing the constraints of existing machine learning models. This study introduces a new predictive model paradigm for patient outcomes and healthcare resource optimisation in machine learning, optimisation, and healthcare analytics. The results show that this integrated strategy could improve healthcare analytics and improve efficiency, effectiveness, and personalisation.

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