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

Hameed, Zeeshan BM and Shah, Milap and Naik, Nithesh and Khanuja, Harneet Singh and Paul, Rahul and Somani, Bhaskar K (2021) 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. JOURNAL OF ENDOUROLOGY, 20 (20). ISSN 08927790

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Abstract

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 data set. Results: The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the stone-free rate with the Minimum Redundancy Maximum Relevance feature (MRMR) treatment extracting top 3 features using Random Forest (RF) was 67%, with MRMR treatment extracting top 5 features using RF was 63%, and with MRMR treatment extracting top 10 features using Decision Tree was 62%. The statistical significance using standard error between the best area under the curves (AUCs) obtained from the Linear Discriminant Analysis (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).

Item Type: Article
Uncontrolled Keywords: kidney calculi, urolithiasis, percutaneous nephrolithotomy, artificial intelligence, decision support system, PCNL
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Medicine > KMC Manipal > Urology
Depositing User: MIT Library
Date Deposited: 11 Nov 2021 10:08
Last Modified: 11 Nov 2021 10:08
URI: http://eprints.manipal.edu/id/eprint/157701

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