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Cited by 15Ovarian function and disordersArtificial Intelligence in HealthcareMobile Health and mHealth Applications

Polycystic Ovary Syndrome Monitoring using Machine Learning

Vasavi, Reddy Richa, Priscilla Prathibha, Seguri, Valiveti, Himabindu, Maringanti, Srinidhi, Parsa, Akhila·2023·2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)
DOI 10.1109/idciot56793.2023.10052781
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Abstract

Polycystic Ovary Syndrome (PCOS) relates to the small cysts that form in the ovaries. PCOS affects health and well-being over the lifespan, rooting out certain critical health issues like high blood pressure, problems with the heart and blood vessels, type 2 diabetes and uterine cancer. Besides, the high widespread of PCOS, many women remain underdiagnosed. Modern Technologies could be used to alter this situation by helping women monitor their lifestyle. The main aim of our project is to build an application to monitor women’s menstruation conditions. The proposed method uses machine learning techniques for predicting the possibility of being diagnosed with a PCOS, based on the symptoms. App Development tools to develop an application to monitor their lifestyle with an additional feature to examine the user’s mental wellness by using GSR (Galvanic Skin Response) sensor.

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Crossref · 10.1109/idciot56793.2023.10052781

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9 of 9 references verified0 citation errors · 0 not checkable

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  • One further study, originating from India and published in 2023, was described in the context of mobile apps, since it reported the development of an app using machine learning and Bluetooth connectivity to a galvanic skin response sensor capable of collecting stress level information.

    “One further study, originating from India and published in 2023, was described in the context of mobile apps [62], since it reported the development of an app using machine learning and Bluetooth connectivity to a galvanic skin response sensor capable of collecting stress level information [62].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [62]

  • A previously described study in the context of mobile apps mentioned that machine learning was used during the app development process.

    “A previously described study in the context of mobile apps mentioned that machine learning was used during the app development process [62].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [62]

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Authors
Vasavi, R. R., Priscilla Prathibha, S., Valiveti, H., Maringanti, S., & Parsa, A.
Affiliations
GRIET,Dept. of ECE,Hyderabad,India
Venue
2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)
Publisher
IEEE
Published
2023-01-05
Pages
1013-1019
DOI
10.1109/idciot56793.2023.10052781
OpenAlex
W4322731576
Cited by
15
References
9
Citations by year
2026: 2 · 2025: 7 · 2024: 5 · 2023: 1
FWCI
18.43
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (publishedVersion)
Topics
  • Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
polycystic ovary syndrome, menstrual cycle monitoring, symptom-based prediction, skin conductance, women's health

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