This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2837) Machine Learning (1748) Clinical Informatics (1168)

Published on in Vol 6, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29967, first published .
Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Authors of this article:

Angela Zigarelli1 Author Orcid Image ;   Ziyang Jia1 Author Orcid Image ;   Hyunsun Lee1 Author Orcid Image
Article Authors Cited by (27) Tweetations (2) Metrics

Journals

  1. Khanna V, Chadaga K, Sampathila N, Prabhu S, Bhandage V, Hegde G. A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Applied System Innovation 2023;6(2):32 View
  2. Tiwari S, Kane L, Koundal D, Jain A, Alhudhaif A, Polat K, Zaguia A, Alenezi F, Althubiti S. SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning. Expert Systems with Applications 2022;203:117592 View
  3. Lai W, Shen N, Zhu H, He S, Yang X, Lai Q, Li R, Ji S, Chen L. Identifying risk factors for polycystic ovary syndrome in women with epilepsy: A comprehensive analysis of 248 patients. Journal of Neuroendocrinology 2023;35(3) View
  4. S. N, S. N. Analysis of risk factors in diabetics resulted from polycystic ovary syndrome in women by EDA analysis and machine learning techniques. Computer Methods in Biomechanics and Biomedical Engineering 2024;27(1):77 View
  5. Guha S, Kodipalli A. Sensitivity analysis of physical and mental health factors affecting Polycystic ovary syndrome in women. Expert Systems 2023 View
  6. Stafie C, Sufaru I, Ghiciuc C, Stafie I, Sufaru E, Solomon S, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics 2023;13(12):1995 View
  7. Guixue G, Yifu P, Yuan G, Xialei L, Fan S, Qian S, Jinjin X, Linna Z, Xiaozuo Z, Wen F, Wen Y. Progress of the application clinical prediction model in polycystic ovary syndrome. Journal of Ovarian Research 2023;16(1) View
  8. Shahmoradi L, Azadbakht L, Farzi J, Kalhori S, Yazdipour A, Solat F. Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review. BMC Women's Health 2024;24(1) View
  9. Rahman M, Islam A, Islam F, Zaman M, Islam M, Alam Sakib M, Hasan Babu H. Empowering early detection: A web-based machine learning approach for PCOS prediction. Informatics in Medicine Unlocked 2024;47:101500 View
  10. Arora S, Vedpal , Chauhan N. Polycystic Ovary Syndrome (PCOS) diagnostic methods in machine learning: a systematic literature review. Multimedia Tools and Applications 2024;84(16):16301 View
  11. Moral P, Mustafi D, Sahana S. PODBoost: an explainable AI model for polycystic ovarian syndrome detection using grey wolf-based feature selection approach. Neural Computing and Applications 2024;36(30):18627 View
  12. Graca S, Alloh F, Lagojda L, Dallaway A, Kyrou I, Randeva H, Kite C. Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review. Healthcare 2024;12(16):1671 View
  13. He J, Ruan X, Li J. Polycystic ovary syndrome in obstructive sleep apnea-hypopnea syndrome: an updated meta-analysis. Frontiers in Endocrinology 2024;15 View
  14. Gupta D, Gupta A, Sharma S, Kumar S, Rai P, Kaur K, Jindal N. PCOS and Gyno Help: AI Based App and Web Application Development. Journal of The Institution of Engineers (India): Series B 2024 View
  15. Ahmad R, Maghrabi L, Khaja I, Maghrabi L, Ahmad M. SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models. Diagnostics 2024;14(19):2225 View
  16. Chen J, Chen W, Zhu Z, Xu S, Huang L, Tan W, Zhang Y, Zhao Y, Comim F. Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. PLOS ONE 2025;20(1):e0313494 View
  17. Geng R, Guo Y, Wang G, Liu Y, Lv B, Wang H, Yu S. A qualitative evaluation method for the weight of moving objects within a sealed cavity based on time-frequency spectrogram features. The Journal of Supercomputing 2025;81(2) View
  18. Panjwani B, Yadav J, Mohan V, Agarwal N, Agarwal S. Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women. Sensors 2025;25(4):1166 View
  19. Mohi Uddin K, Bhuiyan M, Rahman M, Islam M, Uddin M. Early PCOS Detection: A Comparative Analysis of Traditional and Ensemble Machine Learning Models With Advanced Feature Selection. Engineering Reports 2025;7(2) View
  20. Li M, He Z, Shi L, Lin M, Li M, Cheng Y, Liu H, Xue L, Said K, Yusuf M, Galadanci H, Nie L. Intelligent detection for Polycystic Ovary Syndrome (PCOS): Taxonomy, datasets and detection tools. Computational and Structural Biotechnology Journal 2025;27:1578 View
  21. Nandi A, Singh K, Sharma K. Advancement in early diagnosis of polycystic ovary syndrome: biomarker-driven innovative diagnostic sensor. Microchimica Acta 2025;192(5) View

Books/Policy Documents

  1. Swapnarekha H, Dash P, Nayak J, Routray A. Nature-Inspired Optimization Methodologies in Biomedical and Healthcare. View
  2. Shafik W. AI-Based Nutritional Intervention in Polycystic Ovary Syndrome (PCOS). View

Conference Proceedings

  1. Alshakrani S, Hilal S, Zeki A. 2022 International Conference on Data Analytics for Business and Industry (ICDABI). Hybrid Machine Learning Algorithms for Polycystic Ovary Syndrome Detection View
  2. Gupta K, Prasad R. 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). Polycystic Ovary Syndrome Detection using Deep Learning View
  3. R V, Arya , R K. 2024 International Conference on Inventive Computation Technologies (ICICT). The Development of Polycystic Ovary Syndrome Risk Evaluation System using Advanced Machine Learning Technique View
  4. Sharma K, Kumar R, Verma R, Kaur A, Shah S, Hussien M. 2024 International Conference on Data Science and Network Security (ICDSNS). Enhancing PCOS Prediction Accuracy Through Machine Learning Optimization View