# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 4 Experimental Results  
To evaluate the proposed model for diabetic patients, we use the diabetes 130-US hospitals dataset [33]. The dataset represents ten years (1999-2008) of clinical care at 130 hospitals and integrated delivery networks in the United States. In total, there are 101,766 records (encounters) available for analysis. This data source generally has  
Figure 2: The flowchart of the proposed GA.  
50 attributes (13 attributes are integer types, and 37 attributes are object types). In this research, we use attributes with a missing value percentage of less than 20%. We have also removed constant and quasi-constant attributes for the dataset, as these provide no information for the classification task. Constant attributes are the features that contain a single value for all records in the dataset [43]. Quasi-constant attributes are almost stable features. Here, we consider features quasi-constant with the same value in more than 99.99% of the records. Hospital readmission was stratified into three cohorts: patients who were never readmitted after discharge, those who were readmitted within 30 days of discharge, and those who are readmitted 30 days after discharge (up to a year) [33]. Figure 3 shows the population size of each diabetic patient. As can be seen, 54% of the patients are never readmitted after discharge, resulting in imbalanced data.  
The hospital LOS range varies for different diseases. For diabetic patients, the LOS is between 1 and 14 days. For COVID-19 patients, the LOS is between 1 and 27 days in the dataset. For ICU patients, it is between 1 and 289 days in the MIMIC-III dataset. We create different classes for the LOS on each disease to consider these variations. For diabetic patients, we consider seven classes: 1-2 days, 3-4 days, 5-6 days, 7-8 days, 9-10 days, 11-12 days, and 13-14 days. For COVID-19 patients, we consider these classes: 1-2 days, 3-4 days, 5-6 days, 7-8 days, 9-10 days, 11-12 days, 13-16 days, 17-20 days, and 21-27 days. For the MIMIC-III dataset, we consider the classes the same as the COVID-19 dataset up to a 20-day LOS. However, for a LOS more extended than 20 days, we make these classes: 21-30 days, 31-50 days, 51-80 days, 81-110 days, and longer than 110 days. We have considered 3-day intervals for short-term LOSs similar to the existing research [44; 22; 45]. For the long-term LOS, we considered larger intervals to avoid having many classes. The narrow class division will help hospitals and the healthcare system determine hospital staff and beds for better servicing of patients.  
The distribution of the LOS in each dataset is shown in Figure 4. As can be seen, the density (the number of patients) decreases with the increase in the LOS. One of the most important features that affect patients' LOS is age [46]. Figure 5 demonstrates the relation between LOS and the age of each patient based on the patient's gender for instances with the highest LOS distributions. In Figure 5, each graph shows if LOS will increase or decrease by increasing each patient's age. Based on Figure 5a diabetic patient age has a direct correlation to their LOS in the hospital. By increasing the age, especially over 60 years old, the LOS has increased to more than four days. Figure 5b demonstrates various relations between age and LOS, and there are no clear trends for LOS and age for COVID-19 patients. Figure 5c indicated a high LOS for infants in the emergency room and a direct relation between LOS and age, especially for patients over 18 years old.  
Figure 3: Distributions of the readmission in diabetic patients.  
Figure 4: LOS distribution: (**a**) Diabetes, (**b**) COVID-19, (**c**) MIMIC-III.  
Figure 5: _Cont._