Application of unsupervised analysis techniques to lung cancer patient data.
This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184370&type=printable |
| _version_ | 1826579055748579328 |
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| author | Chip M Lynch Victor H van Berkel Hermann B Frieboes |
| author_facet | Chip M Lynch Victor H van Berkel Hermann B Frieboes |
| author_sort | Chip M Lynch |
| collection | DOAJ |
| description | This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type. Minimal up-front processing of the data enables exploring the out-of-the-box capabilities of established unsupervised learning techniques, with little human intervention through the entire process. The output of the techniques is used to predict survival time, with the efficacy of the prediction representing a proxy for the usefulness of the classification. A basic single variable linear regression against each unsupervised output is applied, and the associated Root Mean Squared Error (RMSE) value is calculated as a metric to compare between the outputs. The results show that self-ordering maps exhibit the best performance, while k-Means performs the best of the simpler classification techniques. Predicting against the full data set, it is found that their respective RMSE values (15.591 for self-ordering maps and 16.193 for k-Means) are comparable to supervised regression techniques, such as Gradient Boosting Machine (RMSE of 15.048). We conclude that unsupervised data analysis techniques may be of use to classify patients by defining the classes as effective proxies for survival prediction. |
| first_indexed | 2024-12-11T11:42:14Z |
| format | Article |
| id | doaj.art-32c64bd59f0e4d0497504af4330fa982 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-14T14:12:12Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-32c64bd59f0e4d0497504af4330fa9822025-02-27T05:36:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018437010.1371/journal.pone.0184370Application of unsupervised analysis techniques to lung cancer patient data.Chip M LynchVictor H van BerkelHermann B FrieboesThis study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type. Minimal up-front processing of the data enables exploring the out-of-the-box capabilities of established unsupervised learning techniques, with little human intervention through the entire process. The output of the techniques is used to predict survival time, with the efficacy of the prediction representing a proxy for the usefulness of the classification. A basic single variable linear regression against each unsupervised output is applied, and the associated Root Mean Squared Error (RMSE) value is calculated as a metric to compare between the outputs. The results show that self-ordering maps exhibit the best performance, while k-Means performs the best of the simpler classification techniques. Predicting against the full data set, it is found that their respective RMSE values (15.591 for self-ordering maps and 16.193 for k-Means) are comparable to supervised regression techniques, such as Gradient Boosting Machine (RMSE of 15.048). We conclude that unsupervised data analysis techniques may be of use to classify patients by defining the classes as effective proxies for survival prediction.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184370&type=printable |
| spellingShingle | Chip M Lynch Victor H van Berkel Hermann B Frieboes Application of unsupervised analysis techniques to lung cancer patient data. PLoS ONE |
| title | Application of unsupervised analysis techniques to lung cancer patient data. |
| title_full | Application of unsupervised analysis techniques to lung cancer patient data. |
| title_fullStr | Application of unsupervised analysis techniques to lung cancer patient data. |
| title_full_unstemmed | Application of unsupervised analysis techniques to lung cancer patient data. |
| title_short | Application of unsupervised analysis techniques to lung cancer patient data. |
| title_sort | application of unsupervised analysis techniques to lung cancer patient data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184370&type=printable |
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