Evaluating Feature Selection Methods to Enhance Diabetes Prediction with Random Forest-Reference-Cited by-同舟云学术

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Evaluating Feature Selection Methods to Enhance Diabetes Prediction with Random Forest

Published:2023-11-23 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the 5th International Conference on Information Management & Machine Intelligence
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Author:

Negi Pushpa¹^ORCID,Dhablia AnishKumar²^ORCID,Vanjari Hrishikesh Bhanudas³^ORCID,Tamkhade Jayashree⁴^ORCID,Ikhar Sharayu⁵^ORCID,Shirkande Shrinivas T.⁶^ORCID

Affiliation:

1. Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University),, India

2. Engineering Manager, Altimetrik India Pvt Ltd, India

3. E&TC Engineering Dept, BVCOE Lavale , SPPU Pune, India

4. E&TC, VIIT, India

5. Department of Information technology, Vishwakarma Institute of Information technology, India

6. Computer Science & Engineering, S.B.Patil College of Engineering Indapur, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3647444.3647934

Reference28 articles.

1. Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms

2. Machine learning for diabetes clinical decision support: a review

3. Prediction of Type 2 Diabetes using Machine Learning Classification Methods

4. Diabetes Prediction: A Deep Learning Approach

5. A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques

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