A review on PCOS syndrome prediction using AI/ML Computation Technology

• Published in: 2024 Second International Conference on Advances in Information Technology (ICAIT) Vol. 1; pp. 1 - 8
• Main Authors: CS, Mrs. Pallavi, S, Soumya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.07.2024
• Subjects: Biomedical imaging; cardiovascular diseases and medical imaging data; Diabetes; Feature extraction; Machine learning algorithms; Polycystic Ovary Syndrome (PCOS); Reviews; Soft sensors; System performance; Technological innovation; Testing; Ultrasonic imaging
• DOI: 10.1109/ICAIT61638.2024.10690536

The new automated detection system for Polycystic Ovary Syndrome (PCOS) diagnosis is the subject of the current invention. Among females that are able to procreate, PCOS is a same endocrine condition marked by irregular menstruation, hormonal imbalance, and cystic ovaries. Preemptive identification and timely innovation are pivotal for effective management and preventing associated complications such as infertility, diabetes, and cardiovascular diseases. The proposed system employs advanced machine learning algorithms and image processing techniques to analyze medical imaging data, including ultrasound scans of ovaries and clinical data such as hormonal profiles and menstrual history. The system utilizes a multimodal approach to integrate various data sources, extracting relevant features indicative of PCOS presence.