Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection
Monitoring and controlling the occurrence of hematoma expansion events after a stroke is a primary clinical focus. The introduction of machine learning (ML) techniques offers intelligent decision support for physicians in this domain. However, for doctors without an ML background, the behavior of a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10376172/ |
| _version_ | 1797361517564264448 |
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| author | Beigeng Zhao Rui Song Xu Guo Lizhi Yu |
| author_facet | Beigeng Zhao Rui Song Xu Guo Lizhi Yu |
| author_sort | Beigeng Zhao |
| collection | DOAJ |
| description | Monitoring and controlling the occurrence of hematoma expansion events after a stroke is a primary clinical focus. The introduction of machine learning (ML) techniques offers intelligent decision support for physicians in this domain. However, for doctors without an ML background, the behavior of a hematoma expansion predictor seems opaque, similar to a “black box.” Moreover, the vast and diverse set of features typically present in medical data acts as a double-edged sword: while encapsulating rich information with potential value, it also includes redundant details that offer little to predictive utility. Comprehensive feature selection is crucial, but many current state-of-the-art hematoma expansion prediction studies based on ML often overlook this step. In this paper, we propose a methodology tailored for comprehensive feature selection across diverse and abundant medical data features and rigorously evaluate ML models. Through experiments on a real-world post-stroke hematoma expansion prediction dataset, we demonstrate the efficacy of our approach in enhancing the performance of ML predictors. Visualization of the associated feature selection process and results further bolsters physicians’ understanding of the model’s decision-making basis, thereby strengthening its interpretability. |
| first_indexed | 2024-03-08T15:54:49Z |
| format | Article |
| id | doaj.art-902a298e50bd4f3ab576cc9c7db0180f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:54:49Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-902a298e50bd4f3ab576cc9c7db0180f2024-01-09T00:04:05ZengIEEEIEEE Access2169-35362024-01-01121688169910.1109/ACCESS.2023.334824410376172Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature SelectionBeigeng Zhao0Rui Song1Xu Guo2Lizhi Yu3College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, ChinaCollege of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, ChinaDepartment of Neurosurgery, Liaoning Cancer Hospital and Institute, Shenyang, ChinaYuhong Sub-Bureau, Shenyang Public Security Bureau, Shenyang, ChinaMonitoring and controlling the occurrence of hematoma expansion events after a stroke is a primary clinical focus. The introduction of machine learning (ML) techniques offers intelligent decision support for physicians in this domain. However, for doctors without an ML background, the behavior of a hematoma expansion predictor seems opaque, similar to a “black box.” Moreover, the vast and diverse set of features typically present in medical data acts as a double-edged sword: while encapsulating rich information with potential value, it also includes redundant details that offer little to predictive utility. Comprehensive feature selection is crucial, but many current state-of-the-art hematoma expansion prediction studies based on ML often overlook this step. In this paper, we propose a methodology tailored for comprehensive feature selection across diverse and abundant medical data features and rigorously evaluate ML models. Through experiments on a real-world post-stroke hematoma expansion prediction dataset, we demonstrate the efficacy of our approach in enhancing the performance of ML predictors. Visualization of the associated feature selection process and results further bolsters physicians’ understanding of the model’s decision-making basis, thereby strengthening its interpretability.https://ieeexplore.ieee.org/document/10376172/Hematoma expansionpost-strokemachine learningfeature selectionmodel interpretability |
| spellingShingle | Beigeng Zhao Rui Song Xu Guo Lizhi Yu Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection IEEE Access Hematoma expansion post-stroke machine learning feature selection model interpretability |
| title | Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection |
| title_full | Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection |
| title_fullStr | Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection |
| title_full_unstemmed | Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection |
| title_short | Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection |
| title_sort | bridging interpretability and performance enhanced machine learning based prediction of hematoma expansion post stroke via comprehensive feature selection |
| topic | Hematoma expansion post-stroke machine learning feature selection model interpretability |
| url | https://ieeexplore.ieee.org/document/10376172/ |
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