Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study

• Published in: PLOS digital health 2024-08, Vol.3 (8), p.e0000578
• Main Authors: Iwagami, Masao, Inokuchi, Ryota, Kawakami, Eiryo, Yamada, Tomohide, Goto, Atsushi, Kuno, Toshiki, Hashimoto, Yohei, Michihata, Nobuaki, Goto, Tadahiro, Shinozaki, Tomohiro, Sun, Yu, Taniguchi, Yuta, Komiyama, Jun, Uda, Kazuaki, Abe, Toshikazu, Tamiya, Nanako
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Engineering and Technology; Medicine and Health Sciences; Physical Sciences; Research and Analysis Methods
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000578

It is expected but unknown whether machine-learning models can outperform regression models, such as a logistic regression (LR) model, especially when the number and types of predictor variables increase in electronic health records (EHRs). We aimed to compare the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and LR with the least absolute shrinkage and selection operator (LR-LASSO) for unplanned readmission. We used EHRs of patients discharged alive from 38 hospitals in 2015-2017 for derivation and in 2018 for validation, including basic characteristics, diagnosis, surgery, procedure, and drug codes, and blood-test results. The outcome was 30-day unplanned readmission. We created six patterns of data tables having different numbers of binary variables (that ≥5% or ≥1% of patients or ≥10 patients had) with and without blood-test results. For each pattern of data tables, we used the derivation data to establish the machine-learning and LR models, and used the validation data to evaluate the performance of each model. The incidence of outcome was 6.8% (23,108/339,513 discharges) and 6.4% (7,507/118,074 discharges) in the derivation and validation datasets, respectively. For the first data table with the smallest number of variables (102 variables that ≥5% of patients had, without blood-test results), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the last data table with the largest number of variables (1543 variables that ≥10 patients had, including blood-test results), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720), suggesting that the difference between GBDT and LR-LASSO was small and their 95% confidence intervals overlapped. In conclusion, GBDT generally outperformed LR-LASSO to predict unplanned readmission, but the difference of c-statistic became smaller as the number of variables was increased and blood-test results were used.