“…To solve this classification task, we used lasso logistic regression (LLR) and random forest (RF), and compared their performance depending on category selection, sampling methods, and hyper-parameter selection. …”
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“…None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors). CONCLUSIONS: In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. …”
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“…Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric. …”
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“…Univariate analysis, least absolute shrinkage and selection operator (LASSO), random forest (RF), and partial least square (PLS) were performed, and the receiver operating characteristic (ROC) curves were plotted. …”
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“…Three machine learning models (support vector machine [SVM], random forest [RF], and multi-layer perceptron [MLP]) were constructed that used the clinical indicators above as parameters. …”
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“…Prevalence was significantly higher in ticks from grassland (16.8%) compared to forests (11.4%). CONCLUSIONS: The high level of small mammal diversity in this region of Germany seems to be reflected in a wide variety of genospecies and STs of B. burgdorferi. …”
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“…Then, the stemness-risk model was constructed by weighted gene correlation network analysis (WGCNA), Cox regression and random survival forest analyses, and the most important marker was experimentally verified. …”
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“…Collectively, the present study provides novel insights into the structure, evolution, and functions of the jujube BAM gene family, thus laying a foundation for further exploration of ZjBAM functional mechanisms in response to elevated temperature and drought stress, while opening up avenues for the development of economic forests in arid areas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08630-5.…”
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“…All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. …”
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“…Weighted UniFrac also did not differ by day, and the most influential factor impacting β-diversity was the individual horse (R(2) ≥ 0.24; P = 0.0001). Random forest modeling was unable to accurately predict days within C–W and W–C, but could predict the individual horse based on microbial composition (accuracy: 0.92 ± 0.05). …”
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“…Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. …”
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“…The best performing AUC in the test set was the Catboost model (AUC=0.839), followed by the XGBoost, GBDT, random forest and Adaboost model (AUCs equal to 0.838, 0, 835, 0.832, 0.823, respectively). …”
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“…To identify a result that is an outlier, we inspected the forest plot for spread of the point estimates and the confidence intervals. …”
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“…In sensitivity analysis, the summary estimates were assessed by repeating meta-analysis after omitting one study at a time. Forest plots were used for reporting outcomes in each study and with their 95% CI. …”
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“…The AS risk model of BC was built by Lasso regression, random forest and multivariate Cox regression. The accuracy of the AS risk model and clinicopathological factors were evaluated by time-dependent receiver operating characteristic (ROC) curves. …”
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“…Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. …”
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“…A meta-analysis was performed with a forest plot to determine risk estimates’ heterogeneity with I(2) index, Q-statistic, and p value under a random-effects model. …”
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“…RESULTS: In total, 19 healthy adults with no history of LBP each completed at least one full session of exercises and postures. Random forest and XGBoost (extreme gradient boosting) models performed the best out of the initial set of 9 engineered feature models. …”
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“…Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. …”
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“…We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT. …”
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