“…Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. …”
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“…Integrating quantitative MGMTp methylation levels from pyrosequencing, GTR, and non-SVZ infringement showed the best discriminative ability in the random forest model for derivation and validation set (AUC: 0.937, 0.911, respectively). …”
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“…We used three ML models, namely, extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), to establish the prediction model. …”
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“…Statistical analysis and forest plot were done with R statistical software (version 3.6.1). …”
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“…VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). …”
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“…Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. …”
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“…With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. …”
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“…To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection. …”
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“…The I(2) statistic and Q-test values of the included studies revealed acceptable homogeneity for studied three IL-1 gene polymorphisms (IL-1A−889: I(2) =0%, IL-1B − 511: I(2) = 0%, IL-1B+3954: I(2) = 24%). Forest plot of association between IL-1B−511 gene and ECBL revealed a significant association between 2/2 genotype of IL-1B−511 gene and an increased risk of ECBL (OR = 0.23, 95% CI = 0.09–0.58, P(heterogeneity)= 0.68, I(2) = 0%, and P = 0.002). …”
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“…Based on the univariable Cox regression, random forests were used to establish risk models for OS and DFS. …”
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“…The outcomes of each study were shown in a forest plot in point estimate and 95% confidence interval (CI). …”
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“…Allium species (as well as other members of the Amaryllidaceae) are widespread and diversified, they are adapted to a wide range of habitats from shady forests to open habitats like meadows, steppes, and deserts. …”
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“…Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. …”
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“…The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). …”
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“…The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. …”
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“…Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). …”
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“…Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. …”
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“…We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling. …”
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“…Finally, we used the microbial community data to develop random forest models that predict PMI with an accuracy of approximately ±34 days over a 1- to 9-month time frame of decomposition. …”
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