Showing 1,281 - 1,300 results of 3,801 for search '"Machine learning"', query time: 0.10s Refine Results
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    Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

    Published 2025-01-01
    “…After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. …”
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    Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometerZenodo by Colleen Partida, Jose Lucas Safanelli, Sadia Mannan Mitu, Mohammad Omar Faruk Murad, Yufeng Ge, Richard Ferguson, Keith Shepherd, Jonathan Sanderman

    Published 2025-02-01
    “…All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. …”
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    Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

    Published 2025-02-01
    “…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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    Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning by David C Currow, Magnus Per Ekström, Max Olsson

    Published 2022-06-01
    “…Objective To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning.Design and setting This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019.Participants A total of 475 older men aged 73 years from the VASCOL dataset.Measures Self-perceived health was measured using the first item of the Short Form 12. …”
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    Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis by Clara K Chow, Aravinda Thiagalingam, Rohan Jayasinghe, Sarah Zaman, Stephen Bacchi, Justin Chan, Aashray Gupta, Shaun Evans, Pramesh Kovoor, Brandon Stretton, Jayme Bennetts, Ammar Zaka, Naim Mridha, Joshua Kovoor, Gopal Sivagangabalan, Cecil Mustafiz, Daud Mutahar, Shreyans Sinhal, James Gorcilov, Benjamin Muston, Fabio Ramponi, Dale J Murdoch

    Published 2025-01-01
    “…Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. …”
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