Machine Learning for the Proactive Identification of Polycystic Ovary Syndrome (PCOS): Empowering Women’s Health

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, often remaining undiagnosed until complications like infertility or metabolic disturbances arise. Early detection is crucial for effective management. This research introduces an innovative PCOS diagnosis model employing machine learning algorithms. After a comparative study of machine learning algorithms KNN was showed highest accuracy of 91%. Utilizing user-provided responses to simple yes/no questions, the model offers accessible and user-friendly early warning signs of PCOS, enabling timely medical consultation and intervention. Doctors commonly face late-stage PCOS diagnoses, leading to complex treatments. This research addresses the need for early identification, reducing associated health risks and enhancing the quality of life for affected individuals. This paper proposes a method to track early indicators such as BMI, period health, skin health, and weight patterns, identifying negative patterns and notifying users early, before they manifest as PCOS. This proactive approach can prevent complications, including obesity, insulin resistance, diabetes, infertility, and cardiovascular issues, offering a comprehensive solution to a pressing healthcare concern. By identifying negative patterns and notifying users early, we aim to prevent complications such as obesity, insulin resistance, diabetes, infertility, and cardiovascular issues. This cost-effective method relies solely on non-invasive factors, making it a convenient and accessible tool for early PCOS detection, eliminating the need for laboratory tests or ultrasound results.