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Volume 14, Issue 2 (2026)
Health Educ Health Promot 2026, 14(2): 1001-1017 |
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Received: 2026/06/5 | Accepted: 2026/07/28 | Published: 2026/09/5
Received: 2026/06/5 | Accepted: 2026/07/28 | Published: 2026/09/5
How to cite this article
Yousefi Yegane B., Yousefi Yegane B.. A Machine Learning-Based Laboratory Screening Tool for Empowering Patient Education and Preventive Counseling in Kidney Stone Disease. Health Educ Health Promot 2026; 14 (2) :1001-1017
URL: http://hehp.daneshafarand.org/article-4-87213-en.html
URL: http://hehp.daneshafarand.org/article-4-87213-en.html
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Abstract (40 Views)
Aims: Preventive counseling for kidney stone disease requires accurate risk identification, yet 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: Data from 952 individuals (714 stone formers, 238 controls) with 28 features were analyzed. Particle Swarm Optimization selected features, balancing accuracy and test reduction. An XGBoost model was developed with 5-fold cross-validation. SHAP analysis identified influential predictors.
Findings: The PSO-optimized model reduced tests from 28 to 8 features (71% reduction) with 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: low citrate (potassium citrate), elevated calcium (dietary modification), elevated uric acid (purine reduction), and reduced eGFR (renal evaluation).
Conclusion: This tool enables non-invasive risk identification from routine data, empowering targeted preventive counseling in primary care.
Instrument & Methods: Data from 952 individuals (714 stone formers, 238 controls) with 28 features were analyzed. Particle Swarm Optimization selected features, balancing accuracy and test reduction. An XGBoost model was developed with 5-fold cross-validation. SHAP analysis identified influential predictors.
Findings: The PSO-optimized model reduced tests from 28 to 8 features (71% reduction) with 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: low citrate (potassium citrate), elevated calcium (dietary modification), elevated uric acid (purine reduction), and reduced eGFR (renal evaluation).
Conclusion: This tool enables non-invasive risk identification from routine data, empowering targeted preventive counseling in primary care.
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