Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data
Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorecta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/10/2514 |
| _version_ | 1797473822454054912 |
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| author | Sultan Imangaliyev Jörg Schlötterer Folker Meyer Christin Seifert |
| author_facet | Sultan Imangaliyev Jörg Schlötterer Folker Meyer Christin Seifert |
| author_sort | Sultan Imangaliyev |
| collection | DOAJ |
| description | Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure. |
| first_indexed | 2024-03-09T20:21:50Z |
| format | Article |
| id | doaj.art-ebca56e52cd24510b280eecfaa606be0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T20:21:50Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ebca56e52cd24510b280eecfaa606be02023-11-23T23:46:37ZengMDPI AGDiagnostics2075-44182022-10-011210251410.3390/diagnostics12102514Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome DataSultan Imangaliyev0Jörg Schlötterer1Folker Meyer2Christin Seifert3Institute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, GermanyInstitute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, GermanyInstitute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, GermanyInstitute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, GermanyMost of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.https://www.mdpi.com/2075-4418/12/10/2514gut microbiomemachine learningclassificationinflammatory bowel diseasecolorectal cancerstacked generalization |
| spellingShingle | Sultan Imangaliyev Jörg Schlötterer Folker Meyer Christin Seifert Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data Diagnostics gut microbiome machine learning classification inflammatory bowel disease colorectal cancer stacked generalization |
| title | Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data |
| title_full | Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data |
| title_fullStr | Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data |
| title_full_unstemmed | Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data |
| title_short | Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data |
| title_sort | diagnosis of inflammatory bowel disease and colorectal cancer through multi view stacked generalization applied on gut microbiome data |
| topic | gut microbiome machine learning classification inflammatory bowel disease colorectal cancer stacked generalization |
| url | https://www.mdpi.com/2075-4418/12/10/2514 |
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