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: The cost-adjusted optimal ensemble approach can diagnose abnormal and normal HS with 97% and 88% accuracy, respectively, among employed classifiers. attempted to forecast heart disease in 2019 using the ensemble learning method utilizing the Statlog heart disease dataset available in the UCI ML repository.

Techniques: Comparison, Selection

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: The cost-adjusted optimal ensemble approach can diagnose abnormal and normal HS with 97% and 88% accuracy, respectively, among employed classifiers. attempted to forecast heart disease in 2019 using the ensemble learning method utilizing the Statlog heart disease dataset available in the UCI ML repository.

Techniques: Comparison

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The properties of the datasets obtained from the UCI repository

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The convergence behavior of the OAs on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The stability behavior of the OAs on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets on 50 individual executions

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The Wilcoxon rank-sum test of the OAs with Trader’ s results as the test base

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The ROC curve of the algorithms on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The PR curve of the algorithms on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The AUC bar diagram for ROC curves on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: The AUC bar diagram for PR curves on the a SHD, b HEP, c PAR, d WDBC, e LUNG, f ARRYTM, g PARKINSON, h ARCENE, and i GENEEXPR datasets

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: A voting-based machine learning approach for classifying biological and clinical datasets

doi: 10.1186/s12859-023-05274-4

Figure Lengend Snippet: A comprehensive comparison between the proposed voting-based method and the other previously introduced approaches

Article Snippet: The mentioned computational strategies have been applied to the Cleveland heart disease (CHD) [ ], Statlog heart disease (SHD) [ ], Wisconsin diagnostic breast cancer (WDBC) disease and mammogram datasets [ , ], respectively.

Techniques: Comparison, Selection, Labeling