BibTeX:

@article{IJIRSTV2I12141,
     title={Diabetes Detection Using Deep learning technique},
     author={Ms. Kamble T.P. and Dr.S.T.Patil},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={12},
     pages={342--349},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I12141.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Deep learning is branch of Artificial Intelligence. It has same architecture as neural network but has extra hidden layers. Deep learning had shown more data processing power or capability than the shallow architecture. It had shown more accuracy in results. The Deep network has Restricted Boltzmann machine as basic building block but restricted Boltzmann machine can be used as classifier, feature extractor [1].Today due to modern life style the people have more stress of work, less physical activities, changed eating habits due to this reason people facing many health related problem. The diabetes is one reason behind the death of people. Diabetes may lead to kidney, eye problems heart problem also [2]. Hence it is better to detect Diabetes in early stage to avoid other health risks. In proposed System Deep learning based Restricted Boltzmann machine approach is used to detect whether patient is diabetic or not as Restricted Boltzmann machine is popular for classification and recognition purpose. To detect either patient is having type 1 or type 2 diabetes decision tree technique used.

Deep learning, Restricted Boltzmann machine, Decision Tree Algorithm