Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?
Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in dia...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/16/4015 |
| _version_ | 1797585236687585280 |
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| author | Toygar Tanyel Chandran Nadarajan Nguyen Minh Duc Bilgin Keserci |
| author_facet | Toygar Tanyel Chandran Nadarajan Nguyen Minh Duc Bilgin Keserci |
| author_sort | Toygar Tanyel |
| collection | DOAJ |
| description | Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature. |
| first_indexed | 2024-03-11T00:04:14Z |
| format | Article |
| id | doaj.art-47d52580d53045e488ebdc4df9a5a64e |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T00:04:14Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-47d52580d53045e488ebdc4df9a5a64e2023-11-19T00:31:53ZengMDPI AGCancers2072-66942023-08-011516401510.3390/cancers15164015Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?Toygar Tanyel0Chandran Nadarajan1Nguyen Minh Duc2Bilgin Keserci3Department of Computer Engineering, Yildiz Technical University, Istanbul 34349, TürkiyeDepartment of Radiology, Gleneagles Hospital Kota Kinabalu, Kota Kinabalu 88100, Sabah, MalaysiaDepartment of Radiology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City 700000, VietnamDepartment of Biomedical Engineering, Yildiz Technical University, Istanbul 34349, TürkiyeMachine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.https://www.mdpi.com/2072-6694/15/16/4015posterior fossa pediatric brain tumorsmagnetic resonance imagingmachine learningexploratory data analysiskernel density estimation |
| spellingShingle | Toygar Tanyel Chandran Nadarajan Nguyen Minh Duc Bilgin Keserci Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? Cancers posterior fossa pediatric brain tumors magnetic resonance imaging machine learning exploratory data analysis kernel density estimation |
| title | Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? |
| title_full | Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? |
| title_fullStr | Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? |
| title_full_unstemmed | Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? |
| title_short | Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us? |
| title_sort | deciphering machine learning decisions to distinguish between posterior fossa tumor types using mri features what do the data tell us |
| topic | posterior fossa pediatric brain tumors magnetic resonance imaging machine learning exploratory data analysis kernel density estimation |
| url | https://www.mdpi.com/2072-6694/15/16/4015 |
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