On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets

arXiv:2403.14687v1 Announce Type: new
Abstract: Missing values or data is one popular characteristic of real-world datasets, especially healthcare data. This could be frustrating when using machine learning algorithms on such datasets, simply because most machine learning models perform poorly in the presence of missing values. The aim of this study is to compare the performance of seven imputation techniques, namely Mean imputation, Median Imputation, Last Observation carried Forward (LOCF) imputation, K-Nearest Neighbor (KNN) imputation, Interpolation imputation, Missforest imputation, and Multiple …

abstract aim algorithms arxiv cs.ai cs.lg data datasets healthcare healthcare data imputation machine machine learning machine learning algorithms machine learning models missing values performance popular type values world