POLYCYSTIC OVARY SYNDROME USING MACHINE LEARNING

Abstract

One of the most common hormonal conditions in women of reproductive age is polycystic ovarian syndrome (PCOS). Since pcos is frequently linked to obesity, type 2 diabetes mellitus, and high cholesterol levels, early detection and treatment are crucial. By counting the number of follicles in an ovarian ultrasound image and adding clinical, biochemical, and imaging characteristics, pcos can be automatically detected and patients divided into normal and pcos-affected groups. The body mass index (BMI), hormone levels, and other factors are all taken into consideration by the Support Vector Machine method, which is utilized for categorization.