MathWorks Inc machine leaning toolbox
Machine Leaning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/svm+and+knn+algorithms/Statistics+and+Machine+Learning+Toolbox/10__1039_slash_c7lc00955k-104-3-0
Average 96 stars, based on 1 article reviews

machine leaning toolbox - by Bioz Stars, 2026-10

96/100 stars

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svm learner (KNIME GmbH)

KNIME GmbH svm learner
Svm Learner, supplied by KNIME GmbH, 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+and+knn+algorithms/svm+learner/pmc09265216-123-10-20
Average 90 stars, based on 1 article reviews

svm learner - by Bioz Stars, 2026-10

90/100 stars

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knn (StatLog Inc)

StatLog Inc knn

Comparison of the related literature on the different datasets.

Knn, 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/product/svm+and+knn+algorithms/knn/pmc11041935-26-17-2
Average 90 stars, based on 1 article reviews

knn - by Bioz Stars, 2026-10

90/100 stars

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Image Search Results

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature on the different datasets.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Comparison, Selection

Comparison of the related literature based on different datasets.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature based on different datasets.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Comparison

Hyperparameters tuning of the classifiers using grid search CV.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Hyperparameters tuning of the classifiers using grid search CV.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Activation Assay

Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets applying RF.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying RF.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

svm and knn algorithms Search Results

Image Search Results