IJMTES – Detecting PCOS using Machine Learning

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : Detecting PCOS using Machine Learning

Author’s Name : Namrata Tanwani

Volume 07 Issue 01  2020unnamed

ISSN no:  2348-3121

Page no: 15-20

Abstract –  Polycystic Ovary Syndrome or PCOS is an endocrine disorder that occurs in women of reproductive age. The condition once detected cannot be cured but treatment can help relieve its affects. The exact cause of  PCOS is still unknown but there are certain factors that portray the risk of getting PCOS.  The factors that result in this syndrome are: obesity, insulin resistance, blood pressure, depression, inflammation. While the symptoms include: hirsutism, Oligo-ovulation, acne, heavy bleeding, skin darkening. Using the causes and symptoms, a model is prepared in order to accept them as features and outputs the presence or absence of this condition. The machine learning models used for supervised classification are K-Nearest Neighbor and Logistic Regression .The  reason behind to build multiple models is to find out the best one for the given dataset, in the known scope of knowledge.

Keywords –  K-Nearest Neighbor, Logistic Regression, PCOS, Supervised learning.