Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers
Differential expression study between tumor and non-tumor cells aids lung cancer diagnostic classifications and prognostic prediction at various stages. Support vector machine (SVM) learning is used to categorize the morphology of lung cancer. Logistic regression, random forest, and group lasso-base...
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Springer Cham
2022
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| Online Access: | http://eprints.sunway.edu.my/2995/ https://link.springer.com/book/10.1007/978-3-031-07802-6 https://doi.org/10.1007/978-3-031-07802-6 |
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| author | Pawar, Shrikant Mittal, Karuna Chandrajit, Lahiri * |
| author2 | Rojas, Ignacio |
| author_facet | Rojas, Ignacio Pawar, Shrikant Mittal, Karuna Chandrajit, Lahiri * |
| author_sort | Pawar, Shrikant |
| building | Sunway Campus Library |
| collection | Institutional Repository |
| content_provider | Sunway University |
| content_source | Sunway Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Differential expression study between tumor and non-tumor cells aids lung cancer diagnostic classifications and prognostic prediction at various stages. Support vector machine (SVM) learning is used to categorize the morphology of lung cancer. Logistic regression, random forest, and group lasso-based models are used to model dichotomous outcome variables. The purpose is to take groups of observations and design boundaries to forecast which group future observations belong to base measurements. The performance of these selected regression and classification models using lung cancer prognostic indicators is evaluated in this article. The presented results might guide for further regularizations in classification techniques using known lung carcinoma marker genes. |
| format | Book Section |
| id | my.sunway.eprints.2995 |
| institution | Sunway University |
| publishDate | 2022 |
| publisher | Springer Cham |
| record_format | eprints |
| spelling | my.sunway.eprints.29952024-08-06T00:31:28Z http://eprints.sunway.edu.my/2995/ Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers Pawar, Shrikant Mittal, Karuna Chandrajit, Lahiri * RC Internal medicine Differential expression study between tumor and non-tumor cells aids lung cancer diagnostic classifications and prognostic prediction at various stages. Support vector machine (SVM) learning is used to categorize the morphology of lung cancer. Logistic regression, random forest, and group lasso-based models are used to model dichotomous outcome variables. The purpose is to take groups of observations and design boundaries to forecast which group future observations belong to base measurements. The performance of these selected regression and classification models using lung cancer prognostic indicators is evaluated in this article. The presented results might guide for further regularizations in classification techniques using known lung carcinoma marker genes. Springer Cham Rojas, Ignacio Valenzuela, Olga Rojas, Fernando Herrera, Luis Javier Ortuno, Francisco 2022 Book Section PeerReviewed Pawar, Shrikant and Mittal, Karuna and Chandrajit, Lahiri * (2022) Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers. In: Bioinformatics and Biomedical Engineering. Lecture Notes in Computer Science, Part 2 (13347). Springer Cham, Berlin, pp. 413-418. ISBN 9783031078026 https://link.springer.com/book/10.1007/978-3-031-07802-6 https://doi.org/10.1007/978-3-031-07802-6 |
| spellingShingle | RC Internal medicine Pawar, Shrikant Mittal, Karuna Chandrajit, Lahiri * Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title | Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title_full | Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title_fullStr | Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title_full_unstemmed | Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title_short | Evaluating Performance of Regression and Classification Models Using Known Lung Carcinomas Prognostic Markers |
| title_sort | evaluating performance of regression and classification models using known lung carcinomas prognostic markers |
| topic | RC Internal medicine |
| url | http://eprints.sunway.edu.my/2995/ https://link.springer.com/book/10.1007/978-3-031-07802-6 https://doi.org/10.1007/978-3-031-07802-6 |
| url_provider | http://eprints.sunway.edu.my/ |