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  1. 36341
    “…Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). …”
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    The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models
  2. 36342
    “…Gdaphen can determine the strongest variables that predict gene dosage effects thanks to the General Linear Model (GLM)-based classifiers or determine the most discriminative not linear distributed variables thanks to Random Forest (RF) implementation. Moreover, Gdaphen provides the efficacy of each classifier and several visualization options to fully understand and support the results as easily readable plots ready to be included in publications. …”
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    Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic data
  3. 36343
    “…METHODS: We proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. …”
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    Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach
  4. 36344
    “…PURPOSE: The border between the State of Amapa, Brazil, and French Guiana is mostly primary forest. In the Oyapock basin, socioeconomic circumstances have fueled sex work, gold mining and the circulation of sexually transmitted infections. …”
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    Sexually transmitted infections on the border between Brazil and French Guiana
  5. 36345
    “…We will assess statistical heterogeneity by visually inspecting forest plots and the I(2) statistic. We will synthesise data using the random-effects model. …”
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    Subcutaneous fast-acting insulin analogues, alone or in combination with long-acting insulin, versus intravenous regular insulin infusion in patients with diabetic ketoacidosis: protocol for an updated systematic review and meta-analysis of randomised trials
  6. 36346
    “…In this experiment, three ensemble learning models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were established using data collected in the field for different years and some environmental variables, After the accuracy validation of the model, XGBoost had the highest accuracy of salinity prediction in this study area, with RMSE of 17.62 dS m(−1), R(2) of 0.73 and RPIQ of 2.45 in the test set. …”
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    Investigation of the spatial and temporal variation of soil salinity using Google Earth Engine: a case study at Werigan–Kuqa Oasis, West China
  7. 36347
    “…ML models include an original partial logistic artificial neural network for CRs (PLANNCR original), a PLANNCR with novel specifications in terms of architecture (PLANNCR extended), and a random survival forest for CRs (RSFCR). The clinical endpoint is the time in years between surgery and disease progression (event of interest) or death (competing event). …”
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    Statistical models versus machine learning for competing risks: development and validation of prognostic models
  8. 36348
    “…Differentially abundant analysis revealed that bacterial species pathogenic to humans and animals were highly abundant in urban areas which indicates that host health and fitness might be negatively affected. Random forest models identified Alistipes shahii as the important species driving the changes in fecal microbiome composition. …”
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    Fine-scale spatial variation shape fecal microbiome diversity and composition in black-tailed prairie dogs (Cynomys ludovicianus)
  9. 36349
    “…Forest trees growing in high altitude conditions offer a convenient model for studying adaptation processes. …”
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    Genetic Adaptation of Siberian Larch (Larix sibirica Ledeb.) to High Altitudes
  10. 36350
    “…We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. …”
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    Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning
  11. 36351
    “…Finally, a classificatory analysis based on the Random Forest model was performed to identify significant variables with the ability to differentiate FGR phenotypes. …”
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    Non-invasive Maternal Hemodynamic Assessment to Classify High-Risk Pregnancies Complicated by Fetal Growth Restriction
  12. 36352
    “…That ≈63% accuracy was improved upon by random forest machine learning, but only with ≈3% improvement. …”
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    Logistic Regression and Machine Learning Models for Predicting Whether Intensive Care Patients Who Are Alert and Without Delirium Remain As Such for at Least Two More Days
  13. 36353
    “…Three methods (SVM-RFE, random forest, and LASSO) were used to find genes that overlap in the GEO database. …”
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    LncRNA ERVH48-1 Contributes to the Drug Resistance of Prostate Cancer and Proliferation through Sponging of miR-4784 to the Activation of the Wnt/β-Catenin Pathway
  14. 36354
    “…Logistic regression (LR), support vector machine (SVM), and random forest classification (RFC) models were trained by two different groups of datasets, the group of multi-class features and the group of density features, respectively. …”
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    Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on (18)F-FDG PET
  15. 36355
    “…GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. …”
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    Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI
  16. 36356
    “…Multivariate Cox analysis revealed that low LMR (HR = 1.49, p = 0.041), advanced FIGO stage (HR = 5.25, p < 0.001), and undefined residual disease (HR = 3.77, p = 0.002) were independent factors in predicting poor OS. A forest plot revealed that LMR had better prognostic value in younger EOC patients, patients with BMI ≥ 25 kg/m(2) and albumin ≥ 35 g/L, CA125 ≥ 35 U/L, patients who had undergone optimal surgery, and those who had completed chemotherapy. …”
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    Lymphocyte-to-monocyte ratio after primary surgery is an independent prognostic factor for patients with epithelial ovarian cancer: A propensity score matching analysis
  17. 36357
    “…In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. …”
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    Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning
  18. 36358
    “…Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. …”
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    Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection
  19. 36359
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    Canadian SARS-CoV-2 serological survey using antenatal serum samples: a retrospective seroprevalence study
  20. 36360
    “…In order to determine the benefits of FTL, two experiments were conducted to measure the voluntary feed intake, growth performance, and nitrogen utilization of forest-type (FT) sheep fed rice straw (RS) and supplemented with either Leucaena leucocephala (LEU) or Samanea saman (SAM) or their equal combination (LS). …”
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    Effects of supplementing rice straw with two fodder tree leaves and their combinations on voluntary feed intake, growth, and nitrogen utilization in sheep
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