Smart Health:Detecting Polycystic Ovary Syndrome with Machine Learning

Smart Health:Detecting Polycystic Ovary Syndrome with Machine Learning

Dr. PRATHIBHA G 1, ISTARTHA P D 2, MOHAMMED ATHA 3,RITHESH B R 4, SHAMBHAVI B V 5, SONU PATEL 6

1 Assoc. Professor, Dept. of Information Science & Engineering, Rajeev Institute of Technology, Hassan

2 Information Science & Engineering, Rajeev Institute of Technology, Hassan 3 Information Science & Engineering, Rajeev Institute of Technology, Hassan 4 Information Science & Engineering, Rajeev Institute of Technology, Hassan 5 Information Science & Engineering, Rajeev Institute of Technology, Hassan

6 Information Science & Engineering, Rajeev Institute of Technology, Hassan

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Smart Health Detecting Polycystic Ovary Syndrome With Machine Learning is a system aimed to improve PCOS diagnosis through machine learning. PCOS is a hormonal condition that affects women, causing infertility and metabolic difficulties. This project analyzes patient data and ultrasound images using preprocessing techniques such as data cleansing and feature extraction. CNN, SVM, and KNN are machine learning algorithms that identify patients as PCOS-positive or normal. The technology automates diagnostics, resulting in higher accuracy and earlier detection. By incorporating deep learning, it ensures consistent outcomes and gives healthcare practitioners with an easily accessible interface. This approach is intended to promote quick intervention, reduce long-term health issues, and improve patient outcomes.

Key words: computer vision, image processing, guidance system, and object recognition.