Review



optimizable knn  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc optimizable knn
    All tested classifiers ranked in terms of accuracy (%).
    Optimizable Knn, 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/result/optimizable knn/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    optimizable knn - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects"

    Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

    Journal: Journal of Clinical Medicine

    doi: 10.3390/jcm10225330

    All tested classifiers ranked in terms of accuracy (%).
    Figure Legend Snippet: All tested classifiers ranked in terms of accuracy (%).

    Techniques Used: Plasmid Preparation

    Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.
    Figure Legend Snippet: Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Techniques Used:

    Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.
    Figure Legend Snippet: Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Techniques Used: Plasmid Preparation

    Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.
    Figure Legend Snippet: Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.

    Techniques Used:



    Similar Products

    optimizable knn  (MathWorks Inc)
    90
    MathWorks Inc optimizable knn
    All tested classifiers ranked in terms of accuracy (%).
    Optimizable Knn, 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/result/optimizable knn/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    optimizable knn - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    All tested classifiers ranked in terms of accuracy (%).

    Journal: Journal of Clinical Medicine

    Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

    doi: 10.3390/jcm10225330

    Figure Lengend Snippet: All tested classifiers ranked in terms of accuracy (%).

    Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

    Techniques: Plasmid Preparation

    Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Journal: Journal of Clinical Medicine

    Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

    doi: 10.3390/jcm10225330

    Figure Lengend Snippet: Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

    Techniques:

    Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Journal: Journal of Clinical Medicine

    Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

    doi: 10.3390/jcm10225330

    Figure Lengend Snippet: Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

    Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

    Techniques: Plasmid Preparation

    Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.

    Journal: Journal of Clinical Medicine

    Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

    doi: 10.3390/jcm10225330

    Figure Lengend Snippet: Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.

    Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

    Techniques: