Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Missing data is one of the most common issues encountered in cleaning process especially when dealing with medical dataset. A real collected dataset prone to be incomplete, inconsistent, noisy and redundant due potential reasons such as human errors, instrumental failures, adverse death. Therefore, accurately deal incomplete data, a sophisticated algorithm proposed impute those missing values. Many machine learning algorithms have been applied plausible However, among all imputation algorithms, KNN has widely adopted an for its robustness simplicity it also promising method outperform other methods. This paper provides comprehensive review different techniques used replace data. The goal bring specific attention improvements existing methods provide readers better grasps technique trends.