A Meta-heuristic LASSO Model for Diabetic Readmission Prediction

Abstract

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 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.

Lu He 何璐

Faculty of Supply Chain Management

My research interests include systematic resource optimization, multitask prediction, and predictive-driven mixed integer programming.