A Machine Learning Approach for Initial Screening of Polycystic Ovarian Syndrome (PCOS)-Reference-Cited by-同舟云学术

A Machine Learning Approach for Initial Screening of Polycystic Ovarian Syndrome (PCOS)

Published:2021 Issue: Volume: Page:517-529
ISSN:2367-3370
Container-title:Innovations in Smart Cities Applications Volume 4
language:
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Author:

Jaralba Joshua Rei^ORCID,Baldovino Renann^ORCID,Co Homer

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-66840-2_39

Reference20 articles.

1. Saravanan, A., Sathiamoorthy, S.: Detection of polycystic ovarian syndrome : a literature Survey. 7, 46–51 (2018)

2. Purnama, B., Wisesti, U.N., Adiwijaya Nhita, F., Gayatri, A., Mutiah, T.: A classification of polycystic ovary syndrome based on follicle detection of ultrasound images. In: 2015 3rd International Conference on Information and Communication Technology ICoICT 2015, pp. 396–401 (2015). https://doi.org/10.1109/ICoICT.2015.7231458

3. Vidya Bharathi, R., Swetha, S., Neerajaa, J., Varsha Madhavica, J., Janani, D.M., Rekha, S.N., Ramya, S., Usha, B.: An epidemiological survey: effect of predisposing factors for PCOS in indian urban and rural population. Middle East Fertil. Soc. J. 22, 313–316 (2017). https://doi.org/10.1016/j.mefs.2017.05.007

4. Zhang, X.Z., Pang, Y.L., Wang, X., Li, Y.H.: Computational characterization and identification of human polycystic ovary syndrome genes. Sci. Rep. 8, 1–7 (2018). https://doi.org/10.1038/s41598-018-31110-4

5. Hart, R., Doherty, D.A.: The potential implications of a PCOS diagnosis on a woman’s long-term health using data linkage. J. Clin. Endocrinol. Metab. 100, 911–919 (2015). https://doi.org/10.1210/jc.2014-3886

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