Using Artificial Intelligence for Diabetic Readmission Prediction
Hospital readmission prediction continues to be a highly encouraged area of investigation mainly because of the readmissions reduction program by the Centers for Medicare and Medicaid services (CMS). The overall goal is to reduce the number of early hospital readmissions by identifying the key risk factors that cause hospital readmissions. This is especially important in Intensive Care Unit (ICU), where patient readmission increases the likelihood of mortality due to the worsening of the patient condition. Traditional approaches use simple logistic regression or other linear classification methods to identify the key features that provide high prediction accuracy.
However, these methods are not sufficient since they cannot capture the complex patterns between different features. In this paper, we propose a hybrid Evolutionary Simulating Annealing LASSO Logistic Regression (ESALOR) model to accurately predict the hospital readmission rate and identify the important risk factors. The proposed model combines the evolutionary simulated annealing method (as seeb below figure) with a sparse logistic regression model of Lasso. The ESALOR model was tested on a publicly available diabetes readmission dataset, and the results show that the proposed model provides better results compared to conventional classification methods including Support Vector Machines (SVM), Decision Tree, Naive Bayes, and Logistic Regression.
Considering the provided information, the proposed model can be summarized in the following steps:
Step 1 Feature Selection: The best subset of features is selected using a combination of filter and wrapper feature selection methods.
Step 2 Formulation: The LASSO-logistic regression formulation of the problem is identified.
Step 3 Initialization: The simulated annealing model is initialized using the evolutionary strategy algorithm.
Step 4 Optimization Level: The parameters (coefficients) of the LASSO model are optimized using a hybrid evolutionary strategy based simulated annealing method. We optimized the parameters of the proposed model.
Step 5 Identifying Solutions: We find the optimal solution by comparing all solutions.
Step 6 Prediction: Hospital readmission of a new patient is predicted using the LASSO model with optimal coefficients.
The results (as seen above table) are compared by looking at performance indicators for readmission, and our models are used to make better predictions. Our approach also shows better results than other approaches in the literature comparing four methods. More specifically, for results of the SVM, ANN, LR and NB, as is seen in Table 2, prediction accuracy is founded around 74 % for testing level. Precision and Recall values are less than 0.7 for most methods. At the same time, F-measure values, which need to be more than 0.8, are founded around 0.65 for these methods. Therefore, when using outstanding methods such as the SVM, ANN, LR and NB, prediction performance is inadequate for readmission.
However, our proposed model’s performance is much better than other methods such as F-measure. It means that the proposed model works for imbalance data because there is no imbalance learning for each subclass. Therefore, the proposed model performs better in predicting the readmission rate.
Conclusion
With the introduction of a reimbursement penalty by the Centers for Medicare and Medicaid (CMS), hospitals have become strongly interested in reducing the readmission rate. In this study, we proposed a hybrid classification framework called Evolutionary Simulating Annealing LASSO Logistic Regression (ESALOR) to improve the classification of readmissions of diabetic patients. The ESALOR model can help health-care providers identify the key risk factors that cause hospital readmission for diabetic patients. By using the identified risk factors, physicians can develop new strategies to reduce readmission rates and costs for the care of individuals with diabetes.
