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A Deep Learning Approach to Identify Diabetes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ramesh, S. Caytiles, Ronnie D. Iyengar, N. Ch. Sriman Narayana |
| Copyright Year | 2017 |
| Abstract | The primary objective of this paper is to predict onset of using deep learning and also as to predict the risk factor and severity of diabetics. The methods are implemented on in a conditional data set of diabetes. The model involves deep learning, in the form of a deep neural network through which we are able to apply predictive analytics on said diabetes data set and obtain optimal results. At the end, a comparative study is done between the implementation of this model on type 1 diabetes mellitus, Pima Indians diabetes and the Rough set theory model. The results add value to additional reports because the number of studies done on diabetes using a deep learning model is few to none. This will help to predict diabetes with much more precision as shown by the results obtained. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://onlinepresent.org/proceedings/vol145_2017/9.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial neural network Biological Neural Networks Deep learning Diabetes Mellitus Diabetes Mellitus, Insulin-Dependent Onset (audio) Pima brand of potassium iodide Rough set Set theory |
| Content Type | Text |
| Resource Type | Article |
