Fig. 5
From: How to develop a meaningful radiomic signature for clinical use in oncologic patients
A heatmap aggregating the performance results of combinations of 6 machine learning models and 9 feature selection techniques. The dataset used for this analysis comprised features extracted from malignant pancreatic neoplasms on diffusion-weighted MRI acquired at high b value images (? of b = 900 s/mm2), which is used to distinguish patients with synchronous liver metastases from those without metastases. The best performing combination was an LDA model with mRMR feature selection method. SVM: Support Vector Machine, GLM: General Linear Model, LDA: Linear Discriminant Analysis, LG: Logistic Regression, NB: Naïve Bayes, KNN: K Nearest Neighbor, FSCR: Fisher Score, TSCR: T-Score, CHSQ: CHI-Square, WLCX: Wilcoxon, Gini: Gini index, MIM: Mutual Information Maximization, mRMR: minimum Redundancy Maximum Relevance, JMI: Joint mutual information