A multi-parameterized artificial neural network for lung cancer risk prediction.
The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6200229?pdf=render |
| _version_ | 1828521831293779968 |
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| author | Gregory R Hart David A Roffman Roy Decker Jun Deng |
| author_facet | Gregory R Hart David A Roffman Roy Decker Jun Deng |
| author_sort | Gregory R Hart |
| collection | DOAJ |
| description | The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification. |
| first_indexed | 2024-12-11T19:56:40Z |
| format | Article |
| id | doaj.art-52f78e5045894b8084b1aee37990a1a5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T19:56:40Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-52f78e5045894b8084b1aee37990a1a52022-12-22T00:52:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011310e020526410.1371/journal.pone.0205264A multi-parameterized artificial neural network for lung cancer risk prediction.Gregory R HartDavid A RoffmanRoy DeckerJun DengThe objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification.http://europepmc.org/articles/PMC6200229?pdf=render |
| spellingShingle | Gregory R Hart David A Roffman Roy Decker Jun Deng A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS ONE |
| title | A multi-parameterized artificial neural network for lung cancer risk prediction. |
| title_full | A multi-parameterized artificial neural network for lung cancer risk prediction. |
| title_fullStr | A multi-parameterized artificial neural network for lung cancer risk prediction. |
| title_full_unstemmed | A multi-parameterized artificial neural network for lung cancer risk prediction. |
| title_short | A multi-parameterized artificial neural network for lung cancer risk prediction. |
| title_sort | multi parameterized artificial neural network for lung cancer risk prediction |
| url | http://europepmc.org/articles/PMC6200229?pdf=render |
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