Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : Detecting PCOS using Machine Learning
Author’s Name : Namrata Tanwani
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.