• Ms.Shravya Salian
• Dr. Shashidhar Kini K

Kidney stones are a common and painful urological disorder affecting millions globally. In this study, we propose a machine learning-based predictive model to detect the likelihood of kidney stone formation using clinical, physiological, and biochemical data. Algorithms including Logistic Regression, Random Forest, and Support Vector Machine were trained and evaluated on features like age, gender, water intake, dietary habits, and medical history. Our results demonstrate high prediction accuracy, offering a decision support tool for early diagnosis and prevention. The proposed model contributes to scalable, cost-effective, and accessible healthcare diagnostics

Kidney Stone, Machine Learning, Healthcare AI, Logistic Regression, Random Forest, SVM, Predictive Modeling, Clinical Diagnosis, Urology.

"KIDNEY STONE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1016-1019, July-2025, Available :http://www.jetir.org/papers/JETIRGX06190.pdf

"KIDNEY STONE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1016-1019, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06190.pdf