Search alternatives:
class classification » based classification (Expand Search), binary classification (Expand Search), _ classification (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
mapk guided » marker guided (Expand Search), image guided (Expand Search)
binary age » binary image (Expand Search), binary edge (Expand Search)
class classification » based classification (Expand Search), binary classification (Expand Search), _ classification (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
mapk guided » marker guided (Expand Search), image guided (Expand Search)
binary age » binary image (Expand Search), binary edge (Expand Search)
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Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA).docx
Published 2025“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”
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Dataset selection process and exclusion criteria.
Published 2020“…****Severance Dataset A: a total of 10,426 cases (40,331 images; 43 disorders; age mean ± SD = 52.1 ± 18.3, male 45.1%) used for the binary classification (cancer or not). …”
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Participants’ demographic characteristics.
Published 2024“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”
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Imaging parameters.
Published 2024“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”