An adaptive Laplacian weight random forest imputation for imbalance and mixed-type data

As the application of information technology in the medical field is resulting in a large amount of medical data. As early withdrawal and refusal of participants, there are a lot of missing values in medical data. Although various processing methods for missing values have been proposed, few methods for those medical data with characteristics of imbalance and mixed-type data. In this work, we proposed an adaptive Laplacian weight random forest, called ALWRF. In ALWRF, feature weights were adjusted dynamically when model constructing, which increases selection probabilities of features with low Laplacian score and high importance. Meanwhile, a random operator is introduced to increase the diversity of trees. Furthermore, we proposed an imputation method based on SMOTE-NC oversampling technology and the ALWRF method for imbalanced and mixed-type data, called SncALWRFI. Meanwhile, Bayesian optimization and cross-validation were employed to search optimal parameters. The experimental results showed that the ALWRF method outperforms random forest and Bayesian optimized random forest in terms of classification and regression accuracy. Further, in the experiment for missing values, the SncALWRFI showed the best imputation accuracy, and it performed high imputation effectiveness in public datasets with characteristics of imbalanced and mixed-type.