“…OBJECTIVE: The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets. …”
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“…LMR-microbiome associations were assessed using multivariable regression model and Random Forest (RF) classifier algorithm. q<0.05 was considered significant when multiple tests were performed RESULTS: The median age of the entire cohort was 17.0 years [IQR 12.0; 24.0], 52.6% were females and 25.4% had LMR>0.025. …”
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“…This study investigated the extent to which recreation visitors’ behaviors and experiences have been impacted by the COVID-19 pandemic within the White Mountain National Forest (WMNF). A modified drop-off pick-up survey method was employed to collect population-level data from WMNF visitors from June to August of 2020 (n=317), at the height of the pandemic. …”
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“…Risk ratios (RRs) with 95% confidence intervals (CI) were reported along with forest plots. The chi-square test was performed to assess for differences between the subgroups. …”
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“…Machine learning algorithm by Random Forest (RF) model was utilized to screen the robust prognostic markers and construct the CD68-based immune-related risk score (IRRS) for predicting disease-free survival (DFS). …”
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“…Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. …”
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“…Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. …”
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“…Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. …”
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“…This fungus lives primarily in moist soil and decomposing matter, and inhabits the mid-west, south-central, southeastern United States as well as the boreal forests of Ontario and Quebec in Canada. Very little is known about the natural habitat and environmental distribution of Blastomyces species in India. …”
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“…A gut microbiota‐based model for classification of sarcopenia was constructed using the random forest model, and its performance was assessed using the area under receiver‐operating characteristic curve (AUC). …”
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“…For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). …”
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“…Study-specific and overall estimates using the log-odds scale were presented using forest plots. Between-study heterogeneity was evaluated using the I(2) statistic. …”
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“…Univariate and multivariate Cox regression analysis were employed to explore the independent risk factors of OS. The forest plots of HRs for OS were generated to show the above outcomes more visually. …”
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“…OBJECTIVE: We qualitatively evaluated barriers and facilitators regarding a portfolio of guided IBIs in green professions (farmers, gardeners, and foresters). METHODS: Interview participants were selected from 2 randomized controlled trials for either the prevention of depression (Prevention of Depression in Agriculturists [PROD-A]) or the reduction of pain interference (Preventive Acceptance and Commitment Therapy for Chronic Pain in Agriculturists [PACT-A]) in green professions. …”
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“…We hence refrained from pooling, and estimated yields and coverage with corresponding 95% confidence intervals (CIs), stratified by country, legal character (mandatory versus voluntary screening), and follow-up scheme (one-off versus repetitive screening) using forest plots for comparison and synthesis. Of 1,170 articles, 24 reports on screening programmes from 7 countries were included, with considerable variation in eligible populations, time intervals of screening, and diagnostic protocols. …”
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“…The model was more accurate than a random forest approach. CONCLUSIONS: We uncovered a simple, reproducible relationship between Reddit users’ reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. …”
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“…For each type of RSB, we compared 8 machine learning (ML) models: multiple logistic regression (MLR), naive Bayes (BYS), linear discriminant analysis (LDA), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), deep learning (DL), and the ensemble model. …”
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