Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.
Focus on predictive algorithm and its performance evaluation is extensively covered in most research studies to determine best or appropriate predictive model with Optimum prediction solution indicated by prediction accuracy score, precision, recall, f1score etc. Prediction accuracy score from perfo...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-11-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000290 |
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| author | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Gaddafi Abdul-Salaam |
| author_facet | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Gaddafi Abdul-Salaam |
| author_sort | Michael Owusu-Adjei |
| collection | DOAJ |
| description | Focus on predictive algorithm and its performance evaluation is extensively covered in most research studies to determine best or appropriate predictive model with Optimum prediction solution indicated by prediction accuracy score, precision, recall, f1score etc. Prediction accuracy score from performance evaluation has been used extensively as the main determining metric for performance recommendation. It is one of the most widely used metric for identifying optimal prediction solution irrespective of dataset class distribution context or nature of dataset and output class distribution between the minority and majority variables. The key research question however is the impact of class inequality on prediction accuracy score in such datasets with output class distribution imbalance as compared to balanced accuracy score in the determination of model performance in healthcare and other real-world application systems. Answering this question requires an appraisal of current state of knowledge in both prediction accuracy score and balanced accuracy score use in real-world applications where there is unequal class distribution. Review of related works that highlight the use of imbalanced class distribution datasets with evaluation metrics will assist in contextualizing this systematic review. |
| format | Article |
| id | doaj-art-b84eaf9881a24df3af5bee151a80c91b |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-b84eaf9881a24df3af5bee151a80c91b2025-08-20T03:37:57ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-11-01211e000029010.1371/journal.pdig.0000290Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.Michael Owusu-AdjeiJames Ben Hayfron-AcquahTwum FrimpongGaddafi Abdul-SalaamFocus on predictive algorithm and its performance evaluation is extensively covered in most research studies to determine best or appropriate predictive model with Optimum prediction solution indicated by prediction accuracy score, precision, recall, f1score etc. Prediction accuracy score from performance evaluation has been used extensively as the main determining metric for performance recommendation. It is one of the most widely used metric for identifying optimal prediction solution irrespective of dataset class distribution context or nature of dataset and output class distribution between the minority and majority variables. The key research question however is the impact of class inequality on prediction accuracy score in such datasets with output class distribution imbalance as compared to balanced accuracy score in the determination of model performance in healthcare and other real-world application systems. Answering this question requires an appraisal of current state of knowledge in both prediction accuracy score and balanced accuracy score use in real-world applications where there is unequal class distribution. Review of related works that highlight the use of imbalanced class distribution datasets with evaluation metrics will assist in contextualizing this systematic review.https://doi.org/10.1371/journal.pdig.0000290 |
| spellingShingle | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Gaddafi Abdul-Salaam Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. PLOS Digital Health |
| title | Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. |
| title_full | Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. |
| title_fullStr | Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. |
| title_full_unstemmed | Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. |
| title_short | Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. |
| title_sort | imbalanced class distribution and performance evaluation metrics a systematic review of prediction accuracy for determining model performance in healthcare systems |
| url | https://doi.org/10.1371/journal.pdig.0000290 |
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