# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 4 Experiments Results
### Medical Side  
The proposed model with mutual_info gives the best performance in several evaluation metrics (accuracy = 99.69, precision = 99.71, recall = 99.69, and F1 score = 99.69). The improved performance returned to the chosen and appropriate model and the selected feature selection, which extracts the essential features. This importance is also affirmed from the medical side. Traditionally, CKD is identified through medical examination with some lab tests, such as urine analysis and blood urea nitrogen (BUN). Notwithstanding the importance of these examinations, there are less common factors that should be considered in CKD prediction that have a significant effect in predicting CKD diseases. The following points summarize the importance of the chosen features in CKD prediction. * Hemoglobin level was previously associated with heart failure, and many studies examined the relation between hemoglobin and kidney diseases and differences in the treatment process, and risks of death. For example, in [61], the authors aggregate the data of 722 adults from health plan records in California, analyze the correlation between hemoglobin level and other kidney functions, and then make the following observations. (i) The death level increased with lower hemoglobin with 95%, and the confidence interval ranged from 1.11 to 1.22 for hemoglobin from 12 to 12.8 g/dL, 94% with CI between 2.18 to 2.44 for hemoglobin (9.0 to 9.9). (ii) Relations are approximately the same for risk of hospitalization. (iii) The outcome of kidney functions significantly changed with hemoglobin level. (iv) This finding has no significant changes with systolic functions. The same is found true in [62]. * Packed cell volume (PVC) also has a significant effect on CKD [63]. In a study that was conducted to investigate the impact of the PVC, as well as reticulocyte count among 96 (62% male and 38% female) subjects aged 24-60 (mean age 35 \(\pm\) 12.8) with CKD. The PVC calculated the Hawksley microhaematocrit centrifuge (Hawksley, UK). The mean PVC among CKD patients was 33 \(\pm\) 7.98 among CKD compared to 37 \(\pm\) 5.11 among control subjects. The difference was statistically significant (\(p\) = 0.001) [63]. * Serum creatine (SC) was utilized in a study conducted on 84 patients to determine whether serum creatine could be considered a marker in CKD. They concluded that there is a positive relationship between SC and EKD (\(p\) < 0.001) [64]. * In terms of red cells (RC), in the following study [65], the authors examined the relation between blood parameters such as WBC, RBC, and CKD. The study was conducted on patients aged 60-70 years with CKD. The results showed that CKD led to significant decrees in RBC count and lymphocyte count at 83.44%, and 76.1%, as well as a small decrease in platelets, counted at 6.28%, and WBC at 48.73% [66]. Urine tests could also be used to find red blood cells, which are used as an indicator of kidney disease (i.e., stone, cyst, failure, and bladder cancer infection) [67]
* Regarding sugar (SC) and diabetes mellitus (DM) and blood glucose random (BGR), several studies were conducted to study the correlation between diabetes mellitus and kidney disease occurrence [68], the authors concluded with several points as follows. (i) Nephrolithiasis, which is a symmetric disorder with CKD, increased with type 2 diabetes and metabolic syndrome. Glycemic control could contribute to delaying the progression of CKD. (ii) Dose adjustment among hypoglycemic patients is crucial. (iii) All drugs that are cleared by the kidney (i.e., glyburide) should be taken with caution, whereas other drugs that could be cleared by the liver and such sodium cotransporter (i.e., inhibitors) need to reduce in dose, practically when GFR < 30 mL/min. * In terms of serum albumin (al), previous studies associate between kidney function decline and albumin. Recently, several studies explored serum albumin as a risk factor for CKD. For example, in [69], authors made a cohort study among CKD aged from 70 to 79, estimating the association through GFR values.