PCOS and Gyno Help: AI Based App and Web Application Development

Polycystic ovarian syndrome (PCOS) is caused due to an imbalance of reproductive hormones which can further lead to the development of cysts and infertility in women. However, there is a lack of awareness regarding PCOS. The focus of this research paper is to develop an Artificial Intelligence based...

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Vydáno v:Journal of the Institution of Engineers (India). Series B, Electrical Engineering, Electronics and telecommunication engineering, Computer engineering Ročník 106; číslo 5; s. 1455 - 1465
Hlavní autoři: Gupta, Deekshita, Gupta, Amisha, Sharma, Simran, Kumar, Sushant, Rai, Prakhar, Kaur, Kanwarpreet, Jindal, Neeru
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.10.2025
Springer Nature B.V
Témata:
Accuracy
Acne
Algorithms
Androgens
Applications programs
Artificial intelligence
Chatbots
Communications Engineering
Diet
Engineering
Females
Hormones
Infertility
Insulin resistance
Machine learning
Menstruation
Metabolism
Mobile communications networks
Mobile computing
Networks
Original Contribution
Ovaries
Ovulation
Physicians
Polycystic ovary syndrome
Support vector machines
Websites
Womens health
Chatbot
PCOS
Random forest
Machine learning
ISSN:2250-2106, 2250-2114
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Popis
Shrnutí:Polycystic ovarian syndrome (PCOS) is caused due to an imbalance of reproductive hormones which can further lead to the development of cysts and infertility in women. However, there is a lack of awareness regarding PCOS. The focus of this research paper is to develop an Artificial Intelligence based mobile application and a website that will be a one-stop solution for all women, irrespective of whether they are suffering from PCOS or not. This will allow women to self-diagnose PCOS by making them aware of the symptoms and bring about necessary changes in their lifestyle. This would also include doctor consultancy services in case they need a gynaecologist and chatbot for handling user queries. A Machine learning (ML) based PCOS predictor has also been deployed for the detection of PCOS. For optimal ML model selection, the accuracy scores of six different algorithms were compared and analyzed. The Random Forest Algorithm was found superior in performance having an accuracy of 87%. A comparative analysis of the proposed work has also been performed with other websites and applications. The distinctive features of the proposed app and website such as PCOS Predictor, Chatbot, Diet Planner, Period Tracker, and Exercise Tracker make it unique and give it an edge over the existing technologies.
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ISSN:2250-2106
2250-2114
DOI:10.1007/s40031-024-01169-x