## 2 Literature Survey  
The use of the decision tree (DT) model for the classification of glomerular filtration rate (GFR) values in the determination of gout disease holds significant promise. Decision trees have been widely utilized in various domains for classification tasks, and their effectiveness in providing interpretable and accurate predictions makes them suitable for medical diagnostics. Decision tree models have been successfully applied in medical research for the determination of disease risk factors and support for clinical diagnosis [17]. Additionally, decision tree models have been used for the classification of cardiac disorders based on electrocardiogram data, demonstrating their potential in medical classification tasks [18]. Moreover, decision tree models have been employed in the analysis of infectious disease distribution status, indicating their capability to describe the level of knowledge distribution based on the decision tree [19]. The use of random forest (RF) models for the classification of glomerular filtration rate (GFR) values in the context of gout disease determination holds significant promise. Random forest models have demonstrated high accuracy and robustness in various domains, including ecology, neuroimaging data analysis, and cardiovascular disease prediction [20; 21; 22]. The use of the logistic regression (LR) model for the classification of glomerular filtration rate (GFR) values in the determination of gout disease presents a significant avenue for research and clinical application. Logistic regression has been widely employed in medical research for disease prediction and risk factor identification. For instance, a study [23] utilized logistic regression to examine the decadal trends of gout and hyperuricemia prevalence in the United States, demonstrating the model's applicability in epidemiological investigations. Furthermore, logistic regression has been used in the identification of gout-related metabolic indices, highlighting its role in understanding the genetic variation associated with gout risk [24]. In addition, logistic regression has been applied in the context of gout-related comorbidities. For example, a study by [25] investigated the association of gout with an increased risk of hypertension and diabetes mellitus among stroke survivors, showcasing the model's utility in exploring disease interrelationships. Moreover, logistic regression has been utilized in the identification of patients with gout, demonstrating its effectiveness in developing questionnaires for epidemiological studies [26]. Furthermore, logistic regression has been employed in the context of gout risk assessment and classification. The authors of [27] demonstrated the independent relationship between higher Healthy Eating Index-2015 scores and a decreased risk of gout, highlighting the model's role in assessing lifestyle factors associated with the disease. Additionally, logistic regression has been used in the context of gout and hyperuricemia risk assessment, showcasing its potential in understanding the relationship between dietary habits and disease susceptibility [27]. In a different study, the aim was to develop a model that predicts future estimated glomerular filtration rate (eGFR) values in individuals with type 2 diabetes and chronic kidney disease (CKD) and validates them externally. The study used data from three large cohorts in Europe to predict how kidney function might change over a 5-year period, based on 13 variables (e.g., hemoglobin A1c, hemoglobin, serum cholesterol, mean arterial pressure, urine albumin-to-creatinine ratio) collected on routine clinic visits. The multivariable linear mixed-effects model was used for estimation in the article. The R\({}^{2}\) score of the model was determined as 0.74 [28]. In a study by [PERSON] et al., a new deep learning model (dlGFR) was developed and tested in comparison with the MDRD and CKD-EPI equations based on traditional statistical methods. They used serum creatinine, age, gender, and race variables to predict GFR.