Application of Data Mining Techniques to Predict the Length of Stay of Hospitalized Patients with Diabetes

Abstract

Diabetes is one of the most critical public health conditions worldwide. It has been shown that patients with diabetes are associated with a longer length of hospital stay (LOS) and increased associated healthcare cost. The uncertainty of diabetic patients' LOS makes it difficult for hospitals to optimize their scheduling process. In this paper, we applied the stacked ensemble method, with deep learning as the meta-learning algorithm, to predict long vs. short LOS for diabetic patients. The obtained results show that stacked ensemble technique is promising in this field because stacking multiple classification learning algorithms resulted in a better predictive performance than that obtained from any of the constituent learning algorithms. Having a reasonable estimate on LOS for patients with diabetes can help in optimizing the use of hospital resources, reducing healthcare cost, and improving diabetic patient satisfaction.