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Showing 1 - 20 results of 85 for search '(( history data models optimization algorithm ) OR ( binary art bayesian optimization algorithm ))', query time: 0.54s Refine Results
  1. 1
    Variants of history models used for training the general model using data of 40 patients.

    Variants of history models used for training the general model using data of 40 patients. by Tomasz Gutowski (17200042)

    Published 2023
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  2. 2
    Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms.

    Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. by Aref Andishgar (18155608)

    Published 2024
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  3. 3
    Simulated trajectories from real data, optimal schedule, and nearest optimal schedule.

    Simulated trajectories from real data, optimal schedule, and nearest optimal schedule. by Samuel Christensen (9922554)

    Published 2020
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  4. 4
    History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>.

    History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. by Karl W. Schulz (14309461)

    Published 2022
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  5. 5
    Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf

    Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf by Luuk A. van Duuren (12001607)

    Published 2022
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  6. 6
    The proposed framework model–HMLFSM.

    The proposed framework model–HMLFSM. by Murad Al-Rajab (10661360)

    Published 2023
    “…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”
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  7. 7
    Comparison of the proposed model, with others in the literature.

    Comparison of the proposed model, with others in the literature. by Murad Al-Rajab (10661360)

    Published 2023
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  8. 8
    Comparison of models performance metrics.

    Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

    Published 2025
    “…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”
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  9. 9
    The number of relevant genes (features) selected by the proposed model on each phase.

    The number of relevant genes (features) selected by the proposed model on each phase. by Murad Al-Rajab (10661360)

    Published 2023
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  10. 10
    Predictive model-building process.

    Predictive model-building process. by Mukhtar Ijaiya (18935122)

    Published 2025
    “…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”
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  11. 11
    Comparative performance metrics of ML models.

    Comparative performance metrics of ML models. by Rajeev Bopche (20208606)

    Published 2024
    “…By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. …”
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  12. 12
    Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genomic Information) and V4 (Medical History).

    Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genom... by Medhat A. Tawfeek (22522087)

    Published 2025
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  13. 13
    Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were empirically determined. The weighting coefficients (β₁, β₂, β₄) for the input vectors were tuned to reflect the relative importance of medical imaging, clinical data, and medical history, respectively. genomic data (β₃) was weighted as 0 in this study due to its unavailability in dataset.The regularization parameter (C) and the kernel parameter (γ) of the SVM are not predetermined but their values are dynamically determined by the PSO optimization process.

    Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were e... by Medhat A. Tawfeek (22522087)

    Published 2025
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  14. 14
    Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalities: clinical data, medical history, and medical imaging. The approximate number of instances of each type of feature is recorded on the Count column: it is an indicator of the scale and variety of the dataset. Datas were separated into training (75 percent), validation (10 percent) and test (15 percent) sets to maintain patient level separation to prevent data leakage.

    Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalit... by Medhat A. Tawfeek (22522087)

    Published 2025
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  15. 15
    Consort diagram for the study.

    Consort diagram for the study. by Nicola Lazzarini (3528197)

    Published 2022
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  16. 16
    Performance confidence intervals.

    Performance confidence intervals. by Nicola Lazzarini (3528197)

    Published 2022
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  17. 17
    Baseline characteristics of patients in the study cohort.

    Baseline characteristics of patients in the study cohort. by Nicola Lazzarini (3528197)

    Published 2022
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  18. 18
    Input features.

    Input features. by Nicola Lazzarini (3528197)

    Published 2022
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  19. 19
    Most important predictive features.

    Most important predictive features. by Nicola Lazzarini (3528197)

    Published 2022
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  20. 20
    Age feature importance.

    Age feature importance. by Nicola Lazzarini (3528197)

    Published 2022
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