Health Education and Health Promotion

en Machine Learning for Kidney Stone Screening and Prevention توصیفی و پیمایشی Descriptive & Survey Aims: Preventive counseling for kidney stone disease requires accurate risk identification; however, traditional interpretation fails to capture complex metabolic interactions. This study aimed to develop a machine learning–based screening tool using routine blood and urine data to identify high-risk individuals and provide actionable insights for patient education. Instrument & Methods: This retrospective stidy analyzed data from 952 individuals (714 stone formers and 238 controls) with 28 features. Particle Swarm Optimization was used to select features while balancing accuracy and test reduction. An XGBoost model was developed using 5-fold cross-validation. SHAP analysis was used to identify influential predictors. Findings: Stone formers had higher body amss index, were more frequently male, and showed significant differences in serum uric acid, creatinine, 24-hour urinary calcium, and citrate compared with healthy controls (all p<0.05). The Particle Swarm Optimization-optimized model reduced the number of tests from 28 to 8 features (71% reduction) while achieving 93.49% accuracy (95% CI: 91.8–95.0%). The final panel included age, blood_phosphorus, blood_uric_acid, blood_creatinine, eGFR, urine_pH, Urine_Calcium_24h, and urine_citrate_24h. SHAP identified modifiable risk factors, including low citrate (potassium citrate), elevated calcium (dietary modification), elevated uric acid (purine reduction), and reduced eGFR (renal evaluation). Conclusion: This tool enables noninvasive risk identification using routine data, supporting targeted preventive counseling in primary care. Nephrolithiasis, Machine Learning , Patient Education as Topic , Preventive Health Services , Particle Swarm Optimization , Clinical Laboratory Techniques 317 322 http://hehp.daneshafarand.org/browse.php?a_code=A-10-1-124&slc_lang=en&sid=4 B. Yousefi Yegane 1003194753284600387984 1003194753284600387984 Yes Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran B. Yousefi Yegane 1003194753284600387985 1003194753284600387985 No Department of Urology, Faculty of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran