## 1 Introduction  
Gouty arthritis (GA) is a prevalent inflammatory disease that significantly impacts joint health and kidney function. GA is one of the most common inflammatory arthrities, causing severe joint pain, loss of function, and deformity [1, 2]. Furthermore, GA is closely associated with metabolic comorbidities that can lead to myocardial infarction, type 2 diabetes, chronic kidney disease, and premature death [3]. High glucose levels, insulin resistance, and metabolic disorders trigger inflammation in the body, leading to the accumulation of MSU crystals and an increase in serum urate levels. In metabolic disorders, changes in insulin levels and insulin resistance can reduce the kidneys' capacity to excreteurate, thereby contributing to the development and severity of GA [4]. If left untreated, the chronic inflammatory process against MSU crystals continues. Patients with gouty arthritis are exposed to local and systemic inflammation, especially during the attack period [5].  
C-reactive protein (CRP), leukocytes, platelets, thrombocytes, and ferritin increase and albumin decreases with systemic inflammation [6]. Hematologic parameters such as albumin and hemoglobin levels and lymphocyte, neutrophil, and platelet counts are determined by laboratory tests that indicate inflammation, nutrition, and prognosis. GA and hyperuricemia are associated with serious renal complications that can affect kidney function [7]. Impaired renal function occurs in half of patients with GA [8].  
One of the important criteria for the detection of Gou by medical methods is recurrent arthritis attacks caused by high serum uric acid levels (hyperuricemia) and monosodium urate (MSU) crystal deposition [9; 10]. The number of attacks on arthritis is one of the important criteria for detecting the disease. Another important criterion is the most common form of inflammatory arthropathy, which is known to cause severe pain and joint changes [11; 12]. In patients with gout arthritis (GA), the presence of systemic inflammation may lead to a decrease in serum albumin levels. Inflammation reduces albumin production in the liver, resulting in lower albumin levels [13]. Additionally, since albumin has the capacity to bind free urates, low albumin levels may lead to an increased amount of free urate in the bloodstream, thereby raising the risk of monosodium urate (MSU) crystal accumulation [14; 15]. This condition may contribute to more severe inflammation and a worsening disease course in GA patients. Furthermore, low albumin levels are associated with impaired organ function, particularly in the kidneys, and a decline in overall health status [16].  
This study applies machine learning (ML) models to analyze demographic, clinical, and laboratory data, aiming to uncover critical biomarkers associated with GA. By integrating ML-based predictive models, this research provides new insights into the disease mechanism and potential risk factors.  
The important points of this study are as follows:  
* Looking only at serum uric acid levels is not always sufficient to diagnose GA. Therefore, a model was developed, which is expected to provide insights into additional risk factors and the progression of GA.
* A different perspective from traditional approaches is offered by analyzing the contribution of multiple biomarkers with ML models instead of only a single biomarker (uric acid).
* Detailed analysis was performed using ML models to identify the factors affecting GA.
* This study focused on examining the biomarkers that play a role in the development of GA and ranking the importance of these factors.
* Based on the available information to date, although there are a few articles regarding the detection and classification of gout disease, there is currently no study conducted using machine learning methods for identifying the factors affecting the disease.