PCOS Disease Prediction Using Machine Learning Algorithms

Abstract

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting reproductive-aged women, characterized by hormonal imbalances, irregular menstrual cycles, and the presence of cysts on the ovaries. One of the most worrying diseases today is Polycystic Ovarian Syndrome (PCOS) which is highly dangerous to an extent of affecting women's reproductive life to a huge extent. The dataset includes a diverse set of features such as age, body mass index (BMI), hormonal levels, menstrual irregularities, and lifestyle factors. We explore various machine learning algorithms, including linear regression, decision tree, and random forests to identify the most effective model for PCOS prediction. [1] The study focuses on the development of a robust and clinically applicable predictive model that can aid healthcare professionals in early identification of individuals at risk of PCOS. The results obtained from this research have the potential to significantly impact the field of women's health by offering a reliable and efficient tool for PCOS prediction. Early identification of individuals at risk can facilitate timely interventions, personalized treatment plans, and improved outcomes. Furthermore, the study contributes to the growing body of literature on the application of machine learning in healthcare, demonstrating its potential as a valuable tool for predictive modelling in complex endocrine disorders such as PCOS.