Advancing Polycystic Ovary Syndrome Diagnosis: A Holistic Ensemble-Based Machine Learning Approach-Reference-Cited by-同舟云学术

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Advancing Polycystic Ovary Syndrome Diagnosis: A Holistic Ensemble-Based Machine Learning Approach

Published:2023-11-02 Issue: Volume: Page:
ISSN:
Container-title:2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)
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Author:

F Mohammed Usman¹,Ayub Afrah¹,K Thirumala Akash²,Ahmed Mohammed Riyaz³,T Nitesh Kumar⁴

Affiliation:

1. School of Computing and Information Technology, REVA University,Bengaluru,India

2. Professional 1 Storage Administrator, DXC Technology,Bengaluru,India

3. HKBK College of Engineering,Bengaluru,India

4. School of Multidisciplinary Studies, REVA University,Bengaluru,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10333972/10333315/10334200.pdf?arnumber=10334200

Reference22 articles.

1. Detection of polycystic ovarian syndrome using follicle recognition technique

2. A clinical decision support system for polycystic ovarian syndrome using red deer algorithm and random forest classifier

3. A roadmap to a clinical prediction model with computational intelligence for pcos;Krishnaveni;International Journal of Management, Technology and Engineering,2019

4. Diving Deep into Deep Learning:History, Evolution, Types and Applications

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