An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset
| Autor: | Xun Wei, Jiekui Zhang, Shaoyin Cheng, Feng Wei, Fan Jiang, Weiwei Liao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Ensemble forecasting
Computer science Diabetes diagnosis business.industry 02 engineering and technology Type 2 diabetes Machine learning computer.software_genre medicine.disease Imbalanced data 020204 information systems Scale (social sciences) Environmental health Diabetes mellitus 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Conf. Computing Frontiers |
| DOI: | 10.1145/3075564.3075576 |
| Popis: | Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively. |
| Databáze: | OpenAIRE |
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