Data Imputation in Healthcare Applications

Abstract

This chapter explores data imputation, an important approach to resolving the pervasive problem of missing data in the medical field. The authors define data imputation precisely and stress its importance in guaranteeing the accuracy and completeness of medical data. They investigate many approaches to data imputation, ranging from conventional methods to cutting-edge approaches that use machine learning. They discuss the difficulties in handling complicated health data and the moral issues that arise. They demonstrate the efficacy of data imputation via real-world scenarios and provide helpful guidance on resolving implementation issues. They consider how new technologies such as deep learning and artificial intelligence may affect data imputation in the healthcare industry as we move to the future. They believe these advancements could revolutionize how we handle missing data, enhancing the quality of healthcare data and its insights