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  1. 36921
    “…Machine learning algorithms included elastic net regression, random forest, support vector machine, and elastic net Cox regression. …”
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    Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study
  2. 36922
    “…Dam-induced initial differentiation was further amplified by nitrogen and methane metabolism, forming an abrupt transition governing nitrate–methane metabolic interaction and gaseous methane sequestration depth. Using a random forest algorithm, we identified damming-sensitive taxa that possess distinctive metabolic strategies, including energy-saving mechanisms, unique motility behavior, and deep-environment preferences. …”
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    Anthropogenic activities mediate stratification and stability of microbial communities in freshwater sediments
  3. 36923
    “…We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. …”
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    Detection of diabetic patients in people with normal fasting glucose using machine learning
  4. 36924
    “…For ML analyses, XGBoost, LightGBM, CatBoost, multi-layer perceptron, random forest, and logistic regression algorithms were utilized. …”
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    Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study
  5. 36925
    “…Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. …”
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    A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications
  6. 36926
    “…Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). …”
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    Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
  7. 36927
    “…BACKGROUND: Agri-silvicultures (ASC) are biocultural practices procuring either the maintenance of wild diversity in predominantly agricultural spaces or introducing agrobiodiversity into forests. In the Mesoamerican region, ASC contribute to food sovereignty and territorial conservation and provide strategies for dealing with global changes. …”
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    Agri-silvicultures of Mexican Arid America
  8. 36928
    “…Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. …”
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    Deep dissection of stemness-related hierarchies in hepatocellular carcinoma
  9. 36929
    “…Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). …”
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    Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
  10. 36930
    “…Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. …”
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    Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques
  11. 36931
    por He, Wen, Fu, Xianghong, Chen, Song
    Publicado 2023
    “…The predictive efficacy of random forest (RF), neural network, and XGBoost models was assessed through an exhaustive suite of performance indicators. …”
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    Advancing polytrauma care: developing and validating machine learning models for early mortality prediction
  12. 36932
    “…The best-performing algorithm was the random forest one, which achieved accuracy = 0.76 ± 0.08, precision = 0.78 ± 0.13, recall = 0.84 ± 0.07, and F1 = 0.80 ± 0.09. …”
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    Machine learning for distinguishing saudi children with and without autism via eye-tracking data
  13. 36933
    “…Based on the filtered features, we developed five models (Clinical Model, GTV Model, GTV-Clinical Model, CTV Model, and CTV-Clinical Model) using the random forest algorithm and evaluated for their accuracy, precision, recall, F1-Score and AUC. …”
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    Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model
  14. 36934
    “…Red and Arctic foxes used habitat differentially, with near-exclusive use of forest patches by red foxes and marine habitats by Arctic foxes. …”
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    Coexistence of two sympatric predators in a transitional ecosystem under constraining environmental conditions: a perspective from space and habitat use
  15. 36935
    “…In doing so, it aided Wanang’s community to develop sustainably, without sacrificing their forest home.…”
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    Health service needs and perspectives of a rainforest conserving community in Papua New Guinea’s Ramu lowlands: a combined clinical and rapid anthropological assessment with parallel treatment of urgent cases
  16. 36936
    “…All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. …”
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    Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters
  17. 36937
    por Omar, Ahmed, Abd El-Hafeez, Tarek
    Publicado 2023
    “…In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. …”
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    Quantum computing and machine learning for Arabic language sentiment classification in social media
  18. 36938
    “…A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. …”
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    Molecular diagnosis of Chagas disease: a systematic review and meta-analysis
  19. 36939
    por Sancar, Nuriye, Tabrizi, Sahar S.
    Publicado 2023
    “…The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant’s metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the models. …”
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    Machine learning approach for the detection of vitamin D level: a comparative study
  20. 36940
    “…The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance. …”
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    Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit
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