Cleansing and Imputation of Body Mass Index Data and Its Impact on a Machine Learning Based Prediction Model

Background: A challenge of using electronic health records for secondary analyses is data quality. Body mass index (BMI) is an important predictor for various diseases but often not documented properly. Objectives: The aim of our study is to perform data cleansing on BMI values and to find the best method for an imputation of missing values in order to increase data quality. Further, we want to assess the effect of changes in data quality on the performance of a prediction model based on machine learning. Methods: After data cleansing on BMI data, we compared machine learning methods and statistical methods in their accuracy of imputed values using the root mean square error. In a second step, we used three variations of BMI data as a training set for a model predicting the occurrence of delirium. Results: Neural network and linear regression models performed best for imputation. There were no changes in model performance for different BMI input data. Conclusion: Although data quality issues may lead to biases, it does not always affect performance of secondary analyses.