Construction of PCOS Prediction Model based on BP Neural Network

Xianqing Hu, Amit Yadav, Asif Khan, Abhishek Pratap Sah, Sami Azam

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

Original languageEnglish
Title of host publication6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings
Place of PublicationNew Jersey
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages885-889
Number of pages5
ISBN (Electronic)9798331522667
DOIs
Publication statusPublished - 2025
Event6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Goathgaun, Nepal
Duration: 7 Jan 20258 Jan 2025

Publication series

Name6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings

Conference

Conference6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025
Country/TerritoryNepal
CityGoathgaun
Period7/01/258/01/25

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