An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
Abstract Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-75896-y |
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| author | Krishnaraj Chadaga Varada Khanna Srikanth Prabhu Niranjana Sampathila Rajagopala Chadaga Shashikiran Umakanth Devadas Bhat K. S. Swathi Radhika Kamath |
| author_facet | Krishnaraj Chadaga Varada Khanna Srikanth Prabhu Niranjana Sampathila Rajagopala Chadaga Shashikiran Umakanth Devadas Bhat K. S. Swathi Radhika Kamath |
| author_sort | Krishnaraj Chadaga |
| collection | DOAJ |
| description | Abstract Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging tests. In recent times, Artificial Intelligence and machine learning have been a boon for medicine. Hence, several supervised learning techniques have been utilized in this research to diagnose appendicitis in pediatric patients. Six heterogeneous searching techniques have been used to perform hyperparameter tuning and optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Hybrid Self-adaptive Bat Algorithm, Firefly Algorithm, Grid Search, and Randomized Search. Further, nine classification metrics were utilized in this study. The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. According to the explainers, length of stay, means vermiform appendix detected on ultrasonography, white blood cells, and appendix diameter were the most crucial markers in detecting appendicitis. The proposed system can be used in hospitals for an early/quick diagnosis and to validate the results obtained by other diagnostic modalities. |
| format | Article |
| id | doaj-art-8da177656dbf4dd291faa7a1cc1f7c4b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8da177656dbf4dd291faa7a1cc1f7c4b2025-01-26T12:35:21ZengNature PortfolioScientific Reports2045-23222024-10-0114111810.1038/s41598-024-75896-yAn interpretable and transparent machine learning framework for appendicitis detection in pediatric patientsKrishnaraj Chadaga0Varada Khanna1Srikanth Prabhu2Niranjana Sampathila3Rajagopala Chadaga4Shashikiran Umakanth5Devadas Bhat6K. S. Swathi7Radhika Kamath8Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Biostatistics, Yale School of Public Health, Yale UniversityDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Medicine, Dr. TMA Pai Hospital, Manipal Academy of Higher EducationDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Social and Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher EducationDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationAbstract Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging tests. In recent times, Artificial Intelligence and machine learning have been a boon for medicine. Hence, several supervised learning techniques have been utilized in this research to diagnose appendicitis in pediatric patients. Six heterogeneous searching techniques have been used to perform hyperparameter tuning and optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Hybrid Self-adaptive Bat Algorithm, Firefly Algorithm, Grid Search, and Randomized Search. Further, nine classification metrics were utilized in this study. The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. According to the explainers, length of stay, means vermiform appendix detected on ultrasonography, white blood cells, and appendix diameter were the most crucial markers in detecting appendicitis. The proposed system can be used in hospitals for an early/quick diagnosis and to validate the results obtained by other diagnostic modalities.https://doi.org/10.1038/s41598-024-75896-y |
| spellingShingle | Krishnaraj Chadaga Varada Khanna Srikanth Prabhu Niranjana Sampathila Rajagopala Chadaga Shashikiran Umakanth Devadas Bhat K. S. Swathi Radhika Kamath An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients Scientific Reports |
| title | An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| title_full | An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| title_fullStr | An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| title_full_unstemmed | An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| title_short | An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| title_sort | interpretable and transparent machine learning framework for appendicitis detection in pediatric patients |
| url | https://doi.org/10.1038/s41598-024-75896-y |
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