Significance of LASSO Regression as Feature Extraction Techniques in Diabetic Detection-Reference-Cited by-同舟云学术

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Significance of LASSO Regression as Feature Extraction Techniques in Diabetic Detection

Published:2023-12-09 Issue: Volume:6 Page:1-4
ISSN:
Container-title:2023 Third International Conference on Smart Technologies, Communication and Robotics (STCR)
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Author:

Chellappan Dinesh¹,Rajaguru Harikumar²

Affiliation:

1. KPR Institute of Engineering and Technology Avinashi Road,Department of Electrical and Electronics Engineering,Arasur Coimbatore -TN,India

2. Bannari Amman Institute of Technology,Department of Electronics and Communication Engineering,Sathyamangalam Erode -TN,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10392286/10396847/10397006.pdf?arnumber=10397006

Reference7 articles.

1. Performance and evaluation of classification data mining techniques in diabetes;Kumar;Int. J. Comput. Sci. Inf. Technol,2015

2. Diabetes comorbidities in low- and middle-income countries: An umbrella review

3. Regression Shrinkage and Selection Via the Lasso

4. Hybrid Gaussian pLSA model and item based collaborative filtering recommendation;Chen;Comput. Eng. Appl.,2010

5. Softmax Discriminant Classifier

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