Predictive Modeling of Patient Outcomes Using Machine Learning Algorithms in Health Informatics

Abstract

The quick development of health informatics technology now utilizes machine learning (ML) methods to improve predictive models that forecast patient results. A comprehensive research analyzes how ML algorithms predict healthcare situations including patient wellness status and hospital re-entry needs and disease advancement tracking. ML models show better ability to predict patient outcomes with higher precision than established statistical solution techniques. The text explores both the practical obstacles related to data quality and interpretability as well as ethical issues faced by ML models. Research confirms that ML demonstrates its ability to transform personalized medical care as well as clinical choice processes.