Application of Machine Learning for Identifying Factors Associated with Renal Function Impairment in Gouty Arthritis Patients

Gouty arthritis (GA) and its association with kidney failure present significant challenges in healthcare, necessitating effective detection and management strategies. GA, characterized by the deposition of monosodium urate crystals in joints and other tissues, leads to inflammation and severe joint pain, often accompanied by metabolic comorbidities such as myocardial infarction and diabetes. Although GA has been widely studied in the medical field, limited research has explored the use of machine learning (ML) to identify key biomarkers affecting disease progression. This study aims to bridge this gap by leveraging ML models for predictive analysis. In this study, machine learning models such as decision trees, random forests, logistic regression, and artificial neural networks were used to classify GA using demographic, clinical, and laboratory data, and, most importantly, to identify the factors that affect GA. The analysis yielded promising results, with the decision tree model achieving the highest accuracy of 92.85%. Moreover, key factors such as urea, creatinine, and hemoglobin levels were identified during the initial attack, shedding light on the pathophysiology of GA. This study demonstrates how ML methods help identify key factors affecting GA and assist in disease management. By leveraging machine learning techniques, it is possible to refine the factors affecting GA and inform personalized interventions, ultimately improving patient care and outcomes.