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West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Main Library
9 a.m. - 8 p.m.
Phone: (843) 805-6930
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 8 p.m.
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Detecting diabetes using machine learning techniques and python GUI.
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- Author(s): Manivannan D.; Manikandan N. K.
- Source:
AIP Conference Proceedings; 2023, Vol. 2523 Issue 1, p1-10, 10p- Subject Terms:
- Source:
- Additional Information
- Abstract: Diabetes is one of the deadly diseases in the world. Because of diabetes, several varieties of disorders may occur in the body, like blindness, urinaryorgan failure, etc. As such case patient needs to visit clinical labs toget their reports after consultation. Due to this, every time, they have to invest their time and currency. The explosive growth of health-related data presented unprecedented opportunities for improving the health of a patient. Machine learning plays an essential role in the healthcare field and is being increasingly applied to healthcare. The proposed model helps to develop a system that predicts the diabetes risk level of a patient in the early stage itself without visiting any clinical labs with an accuracy of 80.5%. Datais collected from Pima Indians Diabetes Dataset (PIDD). Model development is based on the Support Vector Machine algorithm. Also, in this proposed method, the patient's risk level will be predicted with the helpof symptoms which is experiencing, and based on that report is generated. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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