Year: 2025
Type: conference-paper
Abstract: This paper showcases the effectiveness of multiple imputation (MI) using a chained process (MIC) in imputing missing values in an obesity dataset, highlighting its superiority over single imputation methods due to its computational complexity and lack of familiarity. MIC is implemented and its performance is compared to basic statistical imputation techniques. The results show MIC provides lower error (MSE/RMSE) on numeric variables and higher accuracy on categorical variables versus statistical methods. MIC handles both numeric and categorical missing data well, provided column variables are correlated. By providing a template for applying MIC, this project aims to encourage the use of MI and promote awareness of its benefits over a single imputation for missing data problems in medical research.
Source: AIJR Proceedings
Authors: Gopika Venu, A. Sai Gnanika, B. Rajani, A. Yasmeen, K. Kiranmai
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408855903