Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms

Published:2022-05-31 Issue: Volume: Page:
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Container-title:2022 5th International Conference on Engineering Technology and its Applications (IICETA)
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Author:

Hdaib Dana¹,Almajali Noor¹,Alquran Hiam²,Mustafa Wan Azani³,Al-Azzawi Waleed⁴,Alkhayyat Ahmed⁵

Affiliation:

1. Jordan University Of Science And Technology,Department of Biomedical Engineering,Irbid,Jordan

2. Yarmouk University,Biomedical Systems and Medical Informatics Engineering,Irbid,Jordan,21163

3. Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus,Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus,Arau,Perlis,Malaysia,02600

4. Al-Farahidi University,Medical technical college,Baghdad,Iraq

5. The Islamic University,Faculty of Engineering,Najaf,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9888261/9888265/09888677.pdf?arnumber=9888677

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1. A comprehensive machine learning framework with particle swarm optimization for improved polycystic ovary syndrome (PCOS) diagnosis;Engineering Research Express;2024-09-01

2. F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques;PLOS ONE;2024-08-15

3. AI-driven Detection of PCOS for Personalized Health Improvement;2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS);2024-04-18

4. Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms;2024 2nd International Conference on Disruptive Technologies (ICDT);2024-03-15

5. Empowering Women's Health: Machine Learning for PCOS Detection and Prediction;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15