| Title |
Diabetes Diagnosis And Treatment Research Based On Machine Learning |
| ID_Doc |
19864 |
| Authors |
He B.; Shu K.; Zhang H. |
| Year |
2019 |
| Published |
Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 |
| DOI |
http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00151 |
| Abstract |
The remarkable progress of biotechnology and medical science has led to the significant production of biomedical data. Diabetes mellitus (DM), a common chronic disease, has also been generated a large number of medical data in the process of diagnosis and treatment. So the exploration of medical data has became a hotpot. Nowadays, researchers are using machine learning to discover potentially valuable knowledge in medical data more than ever before. The purpose of this study is to using machine learning algorithms and improved deep learning algorithms to explore the potential medical value of diabetic electronic medical records, and then apply to track the patient's condition based on the patient's diagnostic records, and help diabetic patients in areas with poor medical conditions to track their condition and further help them improve their happiness in life. © 2019 IEEE. |
| Author Keywords |
Auxiliary diagnosis and treatment; Deep learning; Diabetes mellitus |
