# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 1 Introduction  
Hospital readmission and length of stay (LOS) play major roles in hospital expenditures. Recently, healthcare systems' main focus is on patients being readmitted to hospitals within a short time frame (mostly on readmissions that occur within 30 days) after discharge [1]. According to the latest report, the United States healthcare system's burden was 41 billion dollars, due to the hospital readmissions of diabetic patients within 30 days [2]. A study in Spain revealed that, while the total annual cost of diabetic patients was EUR 1803.6 per person, the cost of hospitalization for these patients was EUR 801.6 [3]. Another study conducted in the United States showed that the direct annual cost of diabetes is about USD 9595 per person [4]. There are direct and indirect costs of healthcare systems that are related to inpatient hospitalization. Direct medical costs include the costs associated with the services provided at the hospital, such as inpatient stays, intensive care unit (ICU) stays, laboratory tests, and other types of hospital visits. For various diseases, the hospitalization share of the total cost is different. For diabetic patients, 35% of the total cost is considered for hospitalization [5]. The share for swine flu is 40%. The hospitalization cost for COVID-19 patients varies based on age [6]. On average, 92.6% of the total cost for COVID-19 patients is for hospitalization [7]. These facts indicate that readmission time and LOS are responsible for more than 50% of the total cost to patients. Besides the cost to the patients and healthcare systems, long LOS and repeated readmissions also lead to other problems. An increase in LOS downgrades the quality of healthcare services due to an increase in the patients-to-nurses ratio. During the COVID-19 pandemic, it was reported that for every extra patient per nurse, a 7% increase was incurred in the odds of patient failure-to-rescue rates, as well as a 7% increase in the likelihood of dying within 30 days [8]. Recently, with the emergence of COVID-19, the need for hospital beds has increased. The LOS for COVID-19 patients varies based on the level of severity and age group; the LOS of COVID-19 patients increases with age for patients older than 60. However, the LOS for COVID-19 patients in ICUs decreases for people aged 80 years or older due to a higher mortality rate [9]. The hospital readmission of diabetic patients within 30 days increases the risk of contracting COVID-19 [10]. Diabetic patients have a risk factor for hospitalization and a high mortality rate due to COVID-19. According to recent research in China, the COVID-19 mortality rate in diabetes patients is about three-fold more elevated than the general patient mortality rate [10]. Thus, a precise prediction of readmissions and LOS help the healthcare system to manage the availability of hospital beds and quality of service. Predicting LOS and readmission time frames has been investigated by other researchers in recent years. The main focus of previously published research was to use basic machine learning (ML) methods as a simple classifier for predicting the LOS and readmission time frames. Based on reviewed articles in the related work section, the main gap in previous research is the lack of evaluating new hybrid methods and in investigating the possibility of using new feature sets for LOS and readmission time frame predictions. In this research, we propose a hybrid model, which is achieved by a combination of deep learning (DL) and evolutionary algorithms under the name of the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN). The proposed algorithms are evaluated by three different datasets to predict the readmission time frame for diabetic patients and the LOS for diabetic, COVID-19, and ICU patients. Experimental results indicate that the GAOCNN estimates readmissions with a 97.2% accuracy. Furthermore, the accuracy of the GAOCNN for the LOS prediction is 89%, 99.4%, and 94.1% for diabetic, COVID-19, and ICU patients, respectively.