Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases
The mortality rate due to chronic pediatric respiratory diseases is increasing every year and it is important to assess the severity of these diseases. As symptoms of several pediatric respiratory disorders are frequently identical, identification might be difficult due to the ongoing spread of resp...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10794659/ |
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| author | Roshan Kumar V Srirama Krishnaraj Chadaga H Muralikrishna Niranjana Sampathila Srikanth Prabhu Rajagopala Chadaga |
| author_facet | Roshan Kumar V Srirama Krishnaraj Chadaga H Muralikrishna Niranjana Sampathila Srikanth Prabhu Rajagopala Chadaga |
| author_sort | Roshan Kumar |
| collection | DOAJ |
| description | The mortality rate due to chronic pediatric respiratory diseases is increasing every year and it is important to assess the severity of these diseases. As symptoms of several pediatric respiratory disorders are frequently identical, identification might be difficult due to the ongoing spread of respiratory diseases. Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. As a result, pediatric respiratory disease severity can be detected more quickly and accurately. The KBest feature selection method is used initially to get the best fifteen features from the dataset. The random forest classifier performed well with the best accuracy of 96% compared to other classifiers. Shapley Additive Values (SHAP), Explain Like I’m 5 (ELI5), QLattice, and Local Interpretable Model-agnostic Explanations (LIME) are four Explainable Artificial Intelligence (XAI) techniques used to interpret model predictions. The most significant attributes were patient transfer to the intensive care unit, Kaliemia, Creatinine Blood Test, Cyanosis, and Natremia. The promising results suggest integrating ML into pediatric respiratory disease diagnosis for predictive accuracy and improved patient outcomes. |
| format | Article |
| id | doaj-art-d0ebae811c66434d8528c0faf3fbcc09 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0ebae811c66434d8528c0faf3fbcc092024-12-20T00:00:41ZengIEEEIEEE Access2169-35362024-01-011218951518953410.1109/ACCESS.2024.351604510794659Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory DiseasesRoshan Kumar0https://orcid.org/0009-0001-8930-2229V Srirama1Krishnaraj Chadaga2H Muralikrishna3https://orcid.org/0000-0002-6340-4227Niranjana Sampathila4https://orcid.org/0000-0002-3345-360XSrikanth Prabhu5https://orcid.org/0000-0002-3826-1084Rajagopala Chadaga6Department of Information Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Manipal, Karnataka, IndiaDepartment of Information Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaThe mortality rate due to chronic pediatric respiratory diseases is increasing every year and it is important to assess the severity of these diseases. As symptoms of several pediatric respiratory disorders are frequently identical, identification might be difficult due to the ongoing spread of respiratory diseases. Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. As a result, pediatric respiratory disease severity can be detected more quickly and accurately. The KBest feature selection method is used initially to get the best fifteen features from the dataset. The random forest classifier performed well with the best accuracy of 96% compared to other classifiers. Shapley Additive Values (SHAP), Explain Like I’m 5 (ELI5), QLattice, and Local Interpretable Model-agnostic Explanations (LIME) are four Explainable Artificial Intelligence (XAI) techniques used to interpret model predictions. The most significant attributes were patient transfer to the intensive care unit, Kaliemia, Creatinine Blood Test, Cyanosis, and Natremia. The promising results suggest integrating ML into pediatric respiratory disease diagnosis for predictive accuracy and improved patient outcomes.https://ieeexplore.ieee.org/document/10794659/Clinical variablesexplainable artificial intelligencemachine learningpediatric respiratory diseases |
| spellingShingle | Roshan Kumar V Srirama Krishnaraj Chadaga H Muralikrishna Niranjana Sampathila Srikanth Prabhu Rajagopala Chadaga Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases IEEE Access Clinical variables explainable artificial intelligence machine learning pediatric respiratory diseases |
| title | Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases |
| title_full | Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases |
| title_fullStr | Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases |
| title_full_unstemmed | Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases |
| title_short | Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases |
| title_sort | using explainable machine learning methods to predict the survivability rate of pediatric respiratory diseases |
| topic | Clinical variables explainable artificial intelligence machine learning pediatric respiratory diseases |
| url | https://ieeexplore.ieee.org/document/10794659/ |
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