Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation

• Published in: Engineering, Technology and Applied Science Research
• Main Author: Abdullah M.A.A.; Jessintha L.; Khuneswari G.P.; Jamil S.A.M.; Olaniran O.R.
• Format: Article
• Language: English
• Published: Dr D. Pylarinos 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211465762&doi=10.48084%2fetasr.8909&partnerID=40&md5=5d6d424469343797eb04c458990d1c96

Model construction is of significant importance for the extraction of information from datasets and the prediction of responses based on predictor variables. The objective of this study is to compare the Multiple Regression (MR) and model averaging approaches in the context of missing data and to validate the effectiveness of the Multiple Imputation (MI) method used to address missing data issues. A comparison was performed between the results obtained from the multiple-imputed data and those derived from the Complete Case (CC) data, using a diabetes dataset from Hospital Besar Alor Setar. Prior to the application of MI and model building, k-fold cross-validation was employed to partition the dataset, resulting in 90% of the data lacking complete covariates for training and 10% of the data comprising complete covariates for testing. Subsequently, MI was applied to the 90% training dataset. Model M115, derived from the multiple-imputed data, was identified as the optimal model for MR. In the model averaging approach, two models were identified as optimal: Model 1 (without interaction variables) and Model 2 (with interaction variables). The first one, exhibited the lowest values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). These results indicate that model averaging, specifically Model 1, is the superior model-building approach for this study, demonstrating improved performance compared to MR and validating the effectiveness of the MI method. © by the authors.