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OBJECTIVE: This study aimed to investigate the role of machine learning (ML) classifiers to determine the most informative multiparametric (mp) magnetic resonance imaging (MRI) features in predicting the treatment outcome of high-intensity focused ultrasound (HIFU) ablation with an immediate nonperfused volume (NPV) ratio of at least 90%. PATIENTS AND METHODS: Seventy-three women who underwent HIFU treatment were divided into groups A (n= 47) and B (n= 26), comprising patients with an NPV ratio of at least 90% and< 90%, respectively. An ensemble feature ranking model was introduced based on the score values assigned to the features by five different ML classifiers to determine the most informative mpMRI features. The relationship between the mpMRI features and the immediate NPV ratio of 90% was evaluated using Pearson’s correlation coefficients. The diagnostic ability of the ML classifiers was evaluated using standard performance metrics, including the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity in eight folds cross-validation. RESULTS: For all the 12 most informative features, the area under receiver operating characteristic curve (AUROC), accuracy, specificity, and sensitivity ranged from 0.5 to 0.97, 0.34 to 0.97, 0.56 to 1.0, and 0.87 to 1.0, respectively. The gradient boosting (GBM) classifier demonstrated the best predictive performance with an

AUROC of 0.95 and accuracy of 0.92, followed by the random forest, AdaBoost, logistic regression, and support vector classifiers, which yielded an AUROC of 0.92, 0.92, 0.83, and 0.78 and accuracy of 0.96, 0.88, 0.84, and 0.84 …