The repository administrator has not yet configured an RDF license.
Imputing Missing Values: Reinforcement Bayesian Regression and Random Forest
Imputing missing data plays a pivotal role in minimizing the biases of knowledge in computational data. The principal purpose of this paper is to establish a better approach to dealing with missing data. Clinical data often contain erroneous data, which cause major drawbacks for analysis. In this paper, we present a new dynamic approach for managing missing data in biomedical databases in order to improve overall modeling accuracy. We propose a reinforcement Bayesian regression model. Furthermore; we compare the Bayesian Regression and the random forest dynamically under a reinforcement approach to minimize the ambiguity of knowledge. Our result indicates that the imputation method of random forest scores better than the Bayesian regression in several cases. At best the reinforcement Bayesian regression scores over 85 under range condition of 5 missing data. The reinforcement Bayesian regression performs over 70 accuracy for imputing missing medical data in overall condition. However; the proposed reinforcement Bayesian regression models imputed missing data on over 70 cases are exactly identical to the missing value, which is remarkably making the advantage of the study. This approach significantly improves the accuracy of imputing missing data for clinical research. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
HTML Summary of #16030 Imputing Missing Values: Reinforcement Bayesian Regression and Random Forest