Predicting hospital readmission of diabetics using deep forest

Diabetes can cause a variety of complications, which also leads to a high rate of repeated admission of patients with diabetes, which greatly increases the pain and financial burden of patients. Higher readmission rates also reduce hospital evaluation and operational efficiency. Therefore, it is urgent to screen out high-risk readmission patients in advance and introduce adjuvant treatment to reduce the probability of readmission. In this study, we propose a deep learning model combining wavelet transform and deep forest to hospital readmission of the diabetic. The proposed model has been tested with real clinical records and compared with several prevalent approaches to patient prediction. The experimental results show that the feature representation transformed by wavelet transform may well represent the original features and the deep forest is able to outperform the state-of-the-art approaches to classify diabetics.