External cohort raw data. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Workflow of Decisionlinnc software. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Internal cohort raw data. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

ROC analysis of model-related variable. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Flow chart of the search process. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Supporting data for "Machine learning based prognosis prediction of intracerebral hemorrhage outcome". by Eunhye Yang (17853260)

“…The resulting prognosis prediction ML model, based on Random Forest algorithm, achieved an overall accuracy of 0.81, with AUROCs of 0.93, 0.84 and 0.95 for good, poor, and death outcomes, respectively. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Clinical Characteristics of Patients. by Yifei Wang (95207)

“…Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.…”

Patient screening information for this study. by Yifei Wang (95207)

“…Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.…”

Data Sheet 1_Machine learning potential predictor of idiopathic pulmonary fibrosis.docx by Chenchun Ding (19231043)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

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P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

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Memory storage behavior. by Chenyi Zhu (9383370)

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Elite search behavior. by Chenyi Zhu (9383370)

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Description of the datasets. by Chenyi Zhu (9383370)

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Collaborative hunting behavior. by Chenyi Zhu (9383370)

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Friedman average rank sum test results. by Chenyi Zhu (9383370)

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IRBMO vs. variant comparison adaptation data. by Chenyi Zhu (9383370)

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Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation by Zhen Chen (129176)

“…PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. …”

Table 2_Harnessing serum VOCs and machine learning for the early detection of MAFLD.xlsx by Xin Li (51274)

“…Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. …”