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  1. 36641
    “…Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. …”
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    Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs
  2. 36642
    “…Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts. …”
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    Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction
  3. 36643
    “…METHODS: We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. …”
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    Identifying factors associated with opioid cessation in a biracial sample using machine learning
  4. 36644
    “…In the TCGA-GBM data, modelBuildR allowed best prognostic separation of patients with highest median overall survival difference (7.51 months) followed a difference of 6.04 months for a random forest based method. CONCLUSIONS: The proposed heuristic is beneficial for the retrieval of features associated with two true groups classified with errors. …”
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    modelBuildR: an R package for model building and feature selection with erroneous classifications
  5. 36645
    “…Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN), and k-nearest neighbor (kNN) were used as classification algorithms. …”
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    Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis
  6. 36646
    “…The pooled proportion with a 95% confidence interval (CI) was presented using tables and forest plots. RESULTS: We screened a total of 195 articles. …”
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    Availability of adequately iodized salt at the household level in Ethiopia: A systematic review and meta-analysis
  7. 36647
    “…To address variation of the patterns, a flexible and robust machine learning workflow was set up, based on random forest classifiers, and comprising three steps: variable selection, parameter optimization, and classification. …”
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    Detection of Mycobacterium avium ssp. paratuberculosis in Cultures From Fecal and Tissue Samples Using VOC Analysis and Machine Learning Tools
  8. 36648
    “…The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. …”
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    Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps
  9. 36649
    “…Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). …”
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    Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer
  10. 36650
    “…A large proportion of LTS cases exhibited tertiary lymphoid tissue (TLT) formation, which has been observed to be a positive prognostic marker in a number of tumor types. Using a Random-Forest variable selection approach, we identified the density of stromal iNOS(+) cells and CD68(+) cells as strong positive and negative prognostic variables, respectively. …”
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    Distinct Stromal and Immune Features Collectively Contribute to Long-Term Survival in Pancreatic Cancer
  11. 36651
    “…We developed 5 ML-assisted models from 22 clinical features using logistic regression (LR), LR optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF). The area under the curve (AUC) was applied to determine the model with the highest discrimination. …”
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    Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis
  12. 36652
    “…Participants were followed-up from birth to age 20 years. Random forest classification algorithms were developed to predict suicide attempt. …”
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    Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood
  13. 36653
    “…Every morning BCAPS generated forecasts of salbutamol sulfate (e.g., Ventolin) inhaler dispensations for the upcoming days in 16 Health Service Delivery Areas (HSDAs) using random forest machine learning. These forecasts were compared with observations over a 63-day study period using different methods including the index of agreement (IOA), which ranges from 0 (no agreement) to 1 (perfect agreement). …”
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    Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season
  14. 36654
    “…Leave-one-out cross-validation using random forest classifier was applied to determine the ability of predicting TBI. …”
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    Cognitive Dysfunction, Brain Volumes, and Traumatic Brain Injury in Homeless Persons
  15. 36655
    “…Correlation analysis showed that the expression of stx2 was negatively correlated with the expression of MS4A1 (R=-0.56, P=0.05) and positively correlated with the expression of LTB (R=0.60, P=0.05). The random forest model and Boruta method revealed that expression of selected immune genes could be predictive indicators of stx2 expression with prediction accuracy of MS4A1 >LTB >CCL21 >CD19. …”
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    Abundance and Expression of Shiga Toxin Genes in Escherichia coli at the Recto-Anal Junction Relates to Host Immune Genes
  16. 36656
    “…Patient and clinical characteristics available preoperatively, intraoperatively, and a combination of both were used as inputs for 5 candidate ML models: logistic regression, support vector machine, random forest, gradient boosting tree (GBT), and deep neural network (DNN). …”
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    Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications
  17. 36657
    “…We also assessed the heterogeneity among studies and publication bias via the I-squared index and forest plots. RESULTS: There was no significant difference between arthroplasty and internal fixation groups in patient mortality at both short-term and long-term points. …”
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    A systematic review and meta-analysis comparing arthroplasty and internal fixation in the treatment of elderly displaced femoral neck fractures
  18. 36658
    “…We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. …”
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    Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging
  19. 36659
    “…Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. …”
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    Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions
  20. 36660
    “…This complex is considered the causal agent of Fusarium dieback, a disease that has severely threatened natural forests, landscape trees, and avocado orchards in the last 8 years. …”
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    Molecular evidence of the avocado defense response to Fusarium kuroshium infection: a deep transcriptome analysis using RNA-Seq
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