Integrating Machine Learning for Personalized Kidney Stone Risk Assessment

Kidney stones are a substantial health risk, and effectively preventing them necessitates precise and individualized evaluations of risk. This work presents a clinical decision assistance system that compares logistic regression, assistance vector machine (SVM) and random forest models. These models employ an extensive range of characteristics, such as CLDN11 genetic information, familial background, dietary habits, and fluid consumption, to offer a complete method for evaluating the risk of kidney stone formation. The models were initially trained using the GesNet dataset, a rich repository of clinical and genetic information about kidney stones. Prospective research was done to assess the system's performance in an actual clinical environment. A fresh group of participants was enlisted, and CLDN11 genetic data and comprehensive clinical information were gathered. The main aim of this study was to assess the efficacy of the integrated machine learning models in predicting the probability of kidney stone formation in this prospective population. The accuracy of the models in predicting individualized risk levels was evaluated using performance criteria such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC‐ROC).