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matlab-based computational algorithm  (MathWorks Inc)


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    MathWorks Inc matlab-based computational algorithm
    Matlab Based Computational Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/computer-based+algorithm/10__1007_slash_s12040___024___02306___2-152-11-4
    Average 90 stars, based on 1 article reviews
    matlab-based computational algorithm - by Bioz Stars, 2026-09
    90/100 stars

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    Article Snippet: Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes.. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identiBcation of underlying patterns in geochemical Cuctuations.. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset.



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    Comparison of available AI applications analyzing ECG.

    Journal: Healthcare

    Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis

    doi: 10.3390/healthcare13040408

    Figure Lengend Snippet: Comparison of available AI applications analyzing ECG.

    Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 mm/mV sensitivity , - ECG computer-based data (Philips DXL-16 algorithm) and the C5.0 ML algorithm - based on decision tree structure: - first step: T voltage I (cut-off: 0.055 mV) - second step: QRS PPK aVL or aVF (cut-off: 1.235 mV and 0.178 mV, respectively) - gradient boosting machine (XGBoost) as the core algorithm - Hyperparameter tuning via grid search , ✓ feature selection using LASSO regression ✓ focused on optimizing traditional ECG criteria ✓ 5-fold cross-validation ✓ employed SHAP values for feature importance analysis.

    Techniques: Comparison, Biomarker Discovery, Diagnostic Assay

    Excluded studies.

    Journal: Healthcare

    Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis

    doi: 10.3390/healthcare13040408

    Figure Lengend Snippet: Excluded studies.

    Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 mm/mV sensitivity , - ECG computer-based data (Philips DXL-16 algorithm) and the C5.0 ML algorithm - based on decision tree structure: - first step: T voltage I (cut-off: 0.055 mV) - second step: QRS PPK aVL or aVF (cut-off: 1.235 mV and 0.178 mV, respectively) - gradient boosting machine (XGBoost) as the core algorithm - Hyperparameter tuning via grid search , ✓ feature selection using LASSO regression ✓ focused on optimizing traditional ECG criteria ✓ 5-fold cross-validation ✓ employed SHAP values for feature importance analysis.

    Techniques:

    Included studies and their characteristics.

    Journal: Healthcare

    Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis

    doi: 10.3390/healthcare13040408

    Figure Lengend Snippet: Included studies and their characteristics.

    Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 mm/mV sensitivity , - ECG computer-based data (Philips DXL-16 algorithm) and the C5.0 ML algorithm - based on decision tree structure: - first step: T voltage I (cut-off: 0.055 mV) - second step: QRS PPK aVL or aVF (cut-off: 1.235 mV and 0.178 mV, respectively) - gradient boosting machine (XGBoost) as the core algorithm - Hyperparameter tuning via grid search , ✓ feature selection using LASSO regression ✓ focused on optimizing traditional ECG criteria ✓ 5-fold cross-validation ✓ employed SHAP values for feature importance analysis.

    Techniques: Biomarker Discovery

    Comparison of AI models utilized for each selected study.

    Journal: Healthcare

    Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis

    doi: 10.3390/healthcare13040408

    Figure Lengend Snippet: Comparison of AI models utilized for each selected study.

    Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 mm/mV sensitivity , - ECG computer-based data (Philips DXL-16 algorithm) and the C5.0 ML algorithm - based on decision tree structure: - first step: T voltage I (cut-off: 0.055 mV) - second step: QRS PPK aVL or aVF (cut-off: 1.235 mV and 0.178 mV, respectively) - gradient boosting machine (XGBoost) as the core algorithm - Hyperparameter tuning via grid search , ✓ feature selection using LASSO regression ✓ focused on optimizing traditional ECG criteria ✓ 5-fold cross-validation ✓ employed SHAP values for feature importance analysis.

    Techniques: Comparison, Activation Assay, Extraction, Biomarker Discovery, Variant Assay, Selection

    Comparison of AI models.

    Journal: Healthcare

    Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis

    doi: 10.3390/healthcare13040408

    Figure Lengend Snippet: Comparison of AI models.

    Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 mm/mV sensitivity , - ECG computer-based data (Philips DXL-16 algorithm) and the C5.0 ML algorithm - based on decision tree structure: - first step: T voltage I (cut-off: 0.055 mV) - second step: QRS PPK aVL or aVF (cut-off: 1.235 mV and 0.178 mV, respectively) - gradient boosting machine (XGBoost) as the core algorithm - Hyperparameter tuning via grid search , ✓ feature selection using LASSO regression ✓ focused on optimizing traditional ECG criteria ✓ 5-fold cross-validation ✓ employed SHAP values for feature importance analysis.

    Techniques: Comparison