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gnb  (StatLog Inc)

 
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    StatLog Inc gnb
    Results of classifiers (in %) without feature selection.
    Gnb, supplied by StatLog 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/result/gnb/product/StatLog Inc
    Average 90 stars, based on 1 article reviews
    gnb - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "An extensive experimental analysis for heart disease prediction using artificial intelligence techniques"

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    Journal: Scientific Reports

    doi: 10.1038/s41598-025-90530-1

    Results of classifiers (in %) without feature selection.
    Figure Legend Snippet: Results of classifiers (in %) without feature selection.

    Techniques Used: Selection

    Results of classifiers (in %) with Information Gain.
    Figure Legend Snippet: Results of classifiers (in %) with Information Gain.

    Techniques Used:

    Results of classifiers (in %) with Chi-Square Test.
    Figure Legend Snippet: Results of classifiers (in %) with Chi-Square Test.

    Techniques Used:

    Results of classifiers (in %) with FDA.
    Figure Legend Snippet: Results of classifiers (in %) with FDA.

    Techniques Used:

    Results of classifiers (in %) with Variance Threshold.
    Figure Legend Snippet: Results of classifiers (in %) with Variance Threshold.

    Techniques Used:

    Results of classifiers (in %) with MAD.
    Figure Legend Snippet: Results of classifiers (in %) with MAD.

    Techniques Used:

    Results of classifiers (in %) with Dispersion Ratio.
    Figure Legend Snippet: Results of classifiers (in %) with Dispersion Ratio.

    Techniques Used: Dispersion

    Results of classifiers (in %) with Relief.
    Figure Legend Snippet: Results of classifiers (in %) with Relief.

    Techniques Used:

    Results of classifiers (in %) with Lasso.
    Figure Legend Snippet: Results of classifiers (in %) with Lasso.

    Techniques Used:

    Results of classifiers (in %) with RF Importance.
    Figure Legend Snippet: Results of classifiers (in %) with RF Importance.

    Techniques Used:

    Results of classifiers (in %) with LDA.
    Figure Legend Snippet: Results of classifiers (in %) with LDA.

    Techniques Used:

    Results of classifiers (in %) with PCA.
    Figure Legend Snippet: Results of classifiers (in %) with PCA.

    Techniques Used:

    Performance of proposed model and state-of-the-art models.
    Figure Legend Snippet: Performance of proposed model and state-of-the-art models.

    Techniques Used:



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    Results of classifiers (in %) without feature selection.
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    Image Search Results


    Results of classifiers (in %) without feature selection.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) without feature selection.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: Selection

    Results of classifiers (in %) with Information Gain.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Information Gain.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Chi-Square Test.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Chi-Square Test.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with FDA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with FDA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Variance Threshold.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Variance Threshold.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with MAD.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with MAD.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Dispersion Ratio.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Dispersion Ratio.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: Dispersion

    Results of classifiers (in %) with Relief.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Relief.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with Lasso.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with Lasso.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with RF Importance.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with RF Importance.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with LDA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with LDA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Results of classifiers (in %) with PCA.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Results of classifiers (in %) with PCA.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques:

    Performance of proposed model and state-of-the-art models.

    Journal: Scientific Reports

    Article Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques

    doi: 10.1038/s41598-025-90530-1

    Figure Lengend Snippet: Performance of proposed model and state-of-the-art models.

    Article Snippet: Models like FCMIM + SVM and GNB also performed well, with FCMIM + SVM on the Cleveland dataset achieving an accuracy of 92.37%, 89% sensitivity, and 98% specificity and with GNB on the Z-Alizadeh Sani, Statlog, and Cardiovascular disease datasets achieving 95.43%, 93.3%, and 73.2% accuracies, 95.84%, 89.2%, and 69.3% sensitivities, 94.44%, 96.7%, and 77% specificities, and 96.77%, 92.1%, and 71.9% F1 scores.

    Techniques: