During past decades, hospital readmissions have been the subject of retrospective surveys and prospective trials with a view to their prevention. Hospital readmission is when a discharged patient gets readmitted to a hospital within a certain period. The need for hospital readmission for certain conditions indicates hospital quality. Identifying patients at high risk early in hospitalization will help to reduce the readmission rate, in that hospitals can focus on preparing readmission for patients at high risk to shorten the length of readmission.

The goal of the analysis is to find the determining factors that lead to higher readmission and correspondingly being able to predict which patients will get readmitted. Therefore, we proposed two research questions:

1. What methods can we use to best predict hospital readmission in this dataset?

2. What are the strongest predictors of hospital readmission in diabetic patients?

We used Logistic Regression, Decision Tree, Random Forest, and XGboost classifiers to predict the readmission rate. Each algorithm was evaluated using 10-fold stratified cross-validation. All of our algorithms are evaluated using the area-under-the-curve (AUC), which is equivalent to the c-statistic in the binary classification scenario. In comparing of four models, it showed the XGBoost worked the best for predicting the admission rate, which achieves the highest accuracy as 0.94, with 0.61 on AUC, and the second-best model is Random Forest, which achieves 0.92 accuracy and 0.94 on AUC. In this study, we also identified the most important factors as the time_in_hospital and number of inpatient stays, number of diagnoses, which appears to associate with the severity of the disease.

This project serves for the final term requirement of DSC 462 data mining 2018 Fall course

Data file: open the .csv file as running in the code in the jupyter notebook. Running the code step by step.

The data file includes three .csv file as follow:

diabetic_data.csv

modified_diabetes1205_beforeEDA.csv

smote_data.csv

• Bhuvan, M.S., Kumar, A., Zafar, A. and Kishore, V., 2016. Identifying diabetic patients with high risk of readmission. arXiv preprint arXiv:1602.04257.
• Mingle, D., Predicting Diabetic Readmission Rates: Moving Beyond HbA1c.
• https://medium.com/berkeleyischool/how-to-use-machine-learning-to-predict-hospital-readmissions-part-1-bd137cbdba07