Applying Deep Learning to Public Health: Using Unbalanced Demographic Data to Predict Thyroid Disorder
| Autor: | Tom Brander, Shih-Yin Chen, Patricia Francis-Lyon, Abdullah Al Khaled, Yasser Attiga, Anaelia Ovalle, John LaGue, Nathan Stott, Gaurika Tyagi |
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| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Lift (data mining) Computer science Thyroid disease Deep learning 02 engineering and technology Disease medicine.disease Machine learning computer.software_genre Thyroid disorder Random forest 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing 030212 general & internal medicine Artificial intelligence business computer Statistic |
| Zdroj: | 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). |
| DOI: | 10.1109/iemcon.2018.8614888 |
| Popis: | This study investigates the use of Deep Neural Learning to predict propensity for disease from demographic information alone, with thyroid disease as the test application. The imbalanced dataset of 747,301 samples contained 13 demographic predictor variables that were not known to be associated with the disease, and had much missing information. A TensorFlow feed-forward neural network was trained to predict thyroid disease. Different activation functions and a variety of up-sampling and down-sampling methods were employed. The lift statistic was used to evaluate success in identifying patients with a propensity for thyroid disease. The DNN model outperformed the Random Forest model with a 36.63% improvement in the lift statistic. These results suggest that deep learning may be successfully employed to select candidates for early intervention for improved health outcomes, utilizing a large dataset with only minimal demographic variables, similar to datasets that are held by the marketing arms of healthcare providers. |
| Databáze: | OpenAIRE |
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