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Remote diagnosis of diabetics patient through speech engine and fuzzy based machine learning algorithm
G.S. Shankar,
Published in Springer
Volume: 23
Issue: 4
Pages: 789 - 798
As recent development of technology, it enables patients to get treatment remotely from doctors through audio conversation. The fourth highest number of death every year is caused by diabetics. Almost 50% to 80% of patients can avoid diabetics if the cause is found at the early stage. In this paper, we propose a new methodology to detect Diabetes at an early stage and recommend few attributes in which the patient needs to be careful in order to avoid diabetics. The proposed methodology makes use of fuzzy logic and kNN classifier to find out the caution attributes and recommends them as soon as possible. The proposed algorithm detects the audio signals from patients or clinical labs to process the data. We implemented our proposed methodology on Pima Indian dataset and compared with existing algorithms and the result shows that our algorithm outperforms existing algorithms. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
About the journal
JournalData powered by TypesetInternational Journal of Speech Technology
PublisherData powered by TypesetSpringer