Evaluating Machine Learning Model Performance in Predicting Polycystic Ovarian Syndrome-Reference-Cited by-同舟云学术

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Evaluating Machine Learning Model Performance in Predicting Polycystic Ovarian Syndrome

Published:2023-11-25 Issue: Volume:7 Page:339-344
ISSN:
Container-title:2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)
language:
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Author:

Ananna Fariha Jannat¹,Khan Afsana²,Ashraf MD Sadi²,Zohora Fatema Tuz¹,Reza Md Tanzim²,Rahman Md Mizanur³

Affiliation:

1. Department of Computer Science and Engineering,Dafoddil Smart City Dhaka

2. BRAC University,Department of Computer Science and Enaineering,Dhaka 1212,Bangladesh

3. Department of Computer Science and Engineering Purbachal American City, Kanchon 1460, Green University of Bangladesh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10456368/10456369/10456391.pdf?arnumber=10456391

Reference10 articles.

1. i-HOPE: Detection And Prediction System For Polycystic Ovary Syndrome (PCOS) Using Machine Learning Techniques

2. Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome

3. Detecting pcos using machine learning;Tanwani;IJMTES-International Journal of Modern Trends in Engineering and Science,2020

4. Exploration on Polycystic Ovarian Syndrome and Data Mining Techniques

5. Computational characterization and identification of human polycystic ovary syndrome genes

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