Application of Artificial Intelligence-based classifiers to predict the outcome measures and stone-free status following percutaneous nephrolithotomy for staghorn calculi: cross-validation of data and estimation of accuracy.

OBJECTIVE To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation. MATERIALS AND METHODS The overall procedure includes data collection and prediction model development. Pre/postoperative variables of 100 patients with staghorn calculus, who underwent PCNL were collected. For feature vector, variables and categories including patient history variables, kidney stone parameters, and laboratory data were considered. The prediction model was developed using machine learning techniques, which include dimensionality reduction and supervised classification. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running the leave-one-patient-out cross-validation approach on the dataset. RESULTS The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the SFR with MRMR treatment extracting top 3 features using random forest was 67%, with MRMR treatment extracting top 5 features using random forest was 63%, with MRMR treatment extracting top 10 features using decision tree was 62%. The statistical significance using standard error between the best AUCs obtained from LDA and MRMR. The results obtained from the LDA approach (0.81 AUC) was statistically significant (p= 0.027, z = 2.21) from the MRMR (0.64 AUC) (p= 0.05). CONCLUSION The promising results of the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.