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Main Library
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Classification system on diabetes prediction using deep learning approach.
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- Author(s): Thaiyalnayaki, S.; Kalaiarasi, G.; Nithya, M.; Padmavathy, R.
- Source:
AIP Conference Proceedings; 2023, Vol. 2523 Issue 1, p1-5, 5p- Subject Terms:
- Source:
- Additional Information
- Abstract: Diabetes is considered one of the deadliest and most persistent diseases that cause glucose levels to increase. Complications can arise if diabetes is left untreated and undiagnosed. Patient visits specialist to determine the results of the patient status are usually to found. No matter how you look at it, the growth of methods to cope with AI is critical. There is a goal in this study to develop a model that can accurately predict the risk of diabetes in a patient population. Decision Tree, SVM, and Naive Bayes are used in this study to detect diabetes. The UCI AI library's Pima Indians Diabetes Database (PIDD) performs experiments. The presentation of each of the three computations is based on various factors, such as precision, accuracy, f-measure, and recall. Precision is measured by comparing instances to categorize the results as normal or abnormal and verify using the Receiver Operating Characteristic (ROC) curves. [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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