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svm classifier  (Kaggle Inc)

 
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    Kaggle Inc svm classifier
    Svm Classifier, supplied by Kaggle 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/svm+classifiers/svm/pmc12127516-757-8-22
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: A Novel Method for Predicting Oncogenic Types of Human Papillomavirus
    Article Snippet: Sahay et al. [ ] used SVM and CNN algorithms to classify cervical cancer risk based on 35 risk factors from 858 patients obtained from Kaggle, achieving 86.66% accuracy with SVM and 80.0% with Convolutional Neural Network (CNN).

    Article Title: Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications
    Article Snippet: Batra et al., 2023, India [ ] , Clinical , 541 (Kaggle) , RF, SVM, LR.

    Article Title: Revolutionizing Lung Cancer Detection: A High‐Accuracy Machine Learning Framework for Early Diagnosis
    Article Snippet: Dritsas and Trigka [ ] , 2022 , UCI (Kaggle) , — , — , — , SVM , Cross‐validation , 95.4%.

    Article Title: AI-quantum framework for accurate infertility risk classification in PCOS patients using EHR data
    Article Snippet: , LR, DT, AB, RF, SVM , Dataset of 541 Patients from Kaggle. , AB and RF achieved highest accuracy with 94%.

    Article Title: Revolutionizing Lung Cancer Detection: A High‐Accuracy Machine Learning Framework for Early Diagnosis
    Article Snippet: Abdullah et al. [ ] , 2021 , UCI (Kaggle) , — , Correlation method , — , SVM , — , 95.56%.

    Article Title: A Novel Method for Predicting Oncogenic Types of Human Papillomavirus.
    Article Snippet: Sahay et al. [53] used SVM and CNN algorithms to classify cervical cancer risk based on 35 risk factors from 858 patients obtained from Kaggle, achieving 86.66% accuracy with SVM and 80.0% with Convolutional Neural Network (CNN).

    Article Title: COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search
    Article Snippet: Shams et al. , Classification , Kaggle 23 , SVM (linear kernel) , Accuracy = 79.30%.



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    Image Search Results


    Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

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    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Biomarker Discovery, Functional Assay

    Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Diagnostic Assay