Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach
Healthcare systems face significant challenges and financial burdens due to patient no-shows, highlighting the need for accurate and interpretable predictive models. This study evaluated the efficacy of twelve classification algorithms that can generate self-explanatory results illustrations across...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10741524/ |
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| author | Khaled M. Toffaha Mecit Can Emre Simsekler Aamna Alshehhi Mohammed Atif Omar |
| author_facet | Khaled M. Toffaha Mecit Can Emre Simsekler Aamna Alshehhi Mohammed Atif Omar |
| author_sort | Khaled M. Toffaha |
| collection | DOAJ |
| description | Healthcare systems face significant challenges and financial burdens due to patient no-shows, highlighting the need for accurate and interpretable predictive models. This study evaluated the efficacy of twelve classification algorithms that can generate self-explanatory results illustrations across four categories: Rule Set classifiers, Rule List classifiers, Rule Tree classifiers, and Algebraic Models, using a real-world dataset, “Brazilian Medical Appointment No Shows”. Analysis across multiple performance metrics revealed significant differences among the algorithms. Advanced models like Tree- Generalized Additive Model (GAM), Fast Interpretable Greedy-Tree Sums (FIGS), Tree Alternating Optimization (TAO) Tree, and RuleFit demonstrated superior predictive capabilities using Over-Sampling and feature selection, achieving an accuracy of 87.53%, AUC 0.87, and F1-score of 0.86, compared to basic tree algorithms like Greedy Tree and C4.5. While Tree-GAM showed high accuracy, it had a significantly longer runtime of approximately 101 seconds. FIGS and TAO Tree offered compelling alternatives with comparable accuracy but significantly reduced computational demands, with runtimes under 1 second. These findings highlight the trade-offs between predictive power, computational efficiency, and practical implementation in healthcare settings. The study also revealed the value of flexible, adaptive architectures in capturing nuanced factors influencing patient no-shows. Overall, these advanced algorithms present accurate and interpretable solutions for forecasting patient no-shows, with FIGS and TAO Tree emerging as particularly effective choices that offer a good balance between predictive insight and practical viability. These insights aim to guide health systems in optimizing patient access and reliability while addressing the complex issue of no-shows, underscoring the importance of considering multiple performance metrics when selecting algorithms for real-world applications. |
| format | Article |
| id | doaj-art-dc5a5434576b41cab859b0f2aed9ffc4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dc5a5434576b41cab859b0f2aed9ffc42024-11-19T00:01:10ZengIEEEIEEE Access2169-35362024-01-011216605816606710.1109/ACCESS.2024.349066210741524Predicting Hospital No-Shows: Interpretable Machine Learning Models ApproachKhaled M. Toffaha0https://orcid.org/0000-0002-7765-5430Mecit Can Emre Simsekler1https://orcid.org/0000-0002-1555-5012Aamna Alshehhi2https://orcid.org/0000-0003-1868-1003Mohammed Atif Omar3https://orcid.org/0000-0001-8833-2947Department of Management Science and Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Management Science and Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Management Science and Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesHealthcare systems face significant challenges and financial burdens due to patient no-shows, highlighting the need for accurate and interpretable predictive models. This study evaluated the efficacy of twelve classification algorithms that can generate self-explanatory results illustrations across four categories: Rule Set classifiers, Rule List classifiers, Rule Tree classifiers, and Algebraic Models, using a real-world dataset, “Brazilian Medical Appointment No Shows”. Analysis across multiple performance metrics revealed significant differences among the algorithms. Advanced models like Tree- Generalized Additive Model (GAM), Fast Interpretable Greedy-Tree Sums (FIGS), Tree Alternating Optimization (TAO) Tree, and RuleFit demonstrated superior predictive capabilities using Over-Sampling and feature selection, achieving an accuracy of 87.53%, AUC 0.87, and F1-score of 0.86, compared to basic tree algorithms like Greedy Tree and C4.5. While Tree-GAM showed high accuracy, it had a significantly longer runtime of approximately 101 seconds. FIGS and TAO Tree offered compelling alternatives with comparable accuracy but significantly reduced computational demands, with runtimes under 1 second. These findings highlight the trade-offs between predictive power, computational efficiency, and practical implementation in healthcare settings. The study also revealed the value of flexible, adaptive architectures in capturing nuanced factors influencing patient no-shows. Overall, these advanced algorithms present accurate and interpretable solutions for forecasting patient no-shows, with FIGS and TAO Tree emerging as particularly effective choices that offer a good balance between predictive insight and practical viability. These insights aim to guide health systems in optimizing patient access and reliability while addressing the complex issue of no-shows, underscoring the importance of considering multiple performance metrics when selecting algorithms for real-world applications.https://ieeexplore.ieee.org/document/10741524/Patient no-showsdecision tree algorithmsgreedy tree classifiersadditive model treeshierarchical shrinkage treeshealthcare analytics |
| spellingShingle | Khaled M. Toffaha Mecit Can Emre Simsekler Aamna Alshehhi Mohammed Atif Omar Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach IEEE Access Patient no-shows decision tree algorithms greedy tree classifiers additive model trees hierarchical shrinkage trees healthcare analytics |
| title | Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach |
| title_full | Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach |
| title_fullStr | Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach |
| title_full_unstemmed | Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach |
| title_short | Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach |
| title_sort | predicting hospital no shows interpretable machine learning models approach |
| topic | Patient no-shows decision tree algorithms greedy tree classifiers additive model trees hierarchical shrinkage trees healthcare analytics |
| url | https://ieeexplore.ieee.org/document/10741524/ |
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